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Semi-Automatic Classification Plugin Documentation

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1. sin sine plus minus product ratio power square root open parenthesis close parenthesis exp natural exponential asin inverse sine cos cosine acos inverse cosine tan tangent atan inverse tangent log natural logarithm np where conditional expression with the syntax np where condition value if value if false 126 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 17 4 3 Output raster The output raster is a tif file with the same spatial resolution and projection of input rasters if input rasters have different spatial resolutions then the highest resolution i e minimum pixel size is used for output raster Use NoData value if checked pixels equal to NoData value will be excluded from the output raster Extent Intersection if checked the extent of output raster equals the intersection of input raster extents i e minimum extent if unchecked the output raster extent will include the extents of input rasters Same as if checked the extent of output raster equals the extent of selected layer Calculate if Expression is green choose the output destination and start the calculation if multiple expressions are entered then multiple outputs are created with the same name and a numerical suffix according to the numerical order of expres
2. Signature ist fle M Training shapefile a ining shapefile x Open Save Reset ROI vu Newshp amp g CI ee e S Civi MCinfo CID Cinfo Color MCID MCinfo ctv Cmo A 1 v1 Buit up 1 Buit 11 Bultup l Buitt up1 Y 272 Veget 2 Trees 22 Vegetation 2 Tees c 3 3 som 26 Soi7 2s E oU 2 e 4 v4 Water 31 Water 42 Water 31 Water T1 S TES Ta is eon Ja E gt la OLARE amp Lis Export Import Add to signature AE cS gc Ro parameters ae od Ve Select clessli con algorithm Range radius Min ROI size Max ROI width e dt Maximum Ukelinood v 0 0000 al 0 006000 605 100 JV Use Macroclass ID M Rapid ROI on band 30 2 __ Automatic refresh ROI Automatic plot E z Redos JI 0 Show P pn SA z Y Display cursor for NDVI v m gt Select qmi Reset x Meo Melo a Apply mask Reset 1 C Macroclass 1 U _ Create vector Classification report cid C info e Perform classification 1 li Class 1 E Save RO v Add sig list do SCP Classification Layers Coordinate 822022 1081726 Scale 1 114 579 v Rotation 0 0 y Render EPSG 32616 Fig 20 15 Preview of the algorithm raster There are several methods for masking clouds during the classification step a simple method for masking clouds is the creation of ROIs Create a new ROI inside a cloud in the image and assign a unique Class ID and the Macroclass ID equals to 0 In fact the MC ID 0 is used by SCP for
3. 207 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 4 load the data in QGIS or open a previously saved QGIS project and repeat all the steps that cause the error in the PI if the issue could be related to the image data please use this sample dataset 5 if an error message appears like the one in the following image copy the whole content of the message in a text file Python error Couldn t load plugin SemiAutomaticClassificationPlugin due to an error when calling its classFactory method Traceback most recent call last File usr lib python2 7 dist packages qgis utils py line 219 in startPlugi plugins packageName package classFactory iface File home user qgis2 python plugins SemiAutomaticClassificationPlugin ini from semiautomaticclassificationplugin import SemiAutomaticClassificationPlu File usr lib python2 7 dist packages qgis utils py line 478 in import mod _builtin_import name globals locals fromlist level j File home user qgis2 python plugins SemiAutomaticClassificationPlugin semiat from ui spectralsignaturedialog import SpectralSignatureDialog M M M M M M J Q close Fig 27 2 Error message 6 open the tab Settings Debug page 132 and uncheck the checkbox Records events in a log file then click the button Export Log file and save the log file which is a text file containing information about the Plugin processes
4. Apply mask optional if checked a mask shapefile can be selected and used for masking the classifica tion i e the part of input image that is outside the mask shapefile will not be classified Reset reset the shapefile mask Create vector optional if checked when Perform classification is clicked a shapefile of the classification is saved inside the same folder and with the same name defined for the classification output conversion to vector can also be performed later in Classification to vector page 121 Classification report optional if checked when Perform classification is clicked a report about the land cover classification is calculated providing the pixel count the percentage and area for each class the report is saved as a csv file in the same folder and with the same name defined for the classification output and the suffix _report in addition the results are shown in the Classification report page 121 Perform classification define a classification output a tif file and perform the image classi fication the qml file of the QGIS style is saved along with the classification 16 5 Classification style 99 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 100 Chapter 16 Classification dock CHAPTER 17 Main Interface Window The Main Interface Window is composed of several tabs grouped in sections Each section contains several functions that are usef
5. 7 open the log file and copy the whole content of the file 8 join the Facebook group or the Google community create a new post and copy the error message and the log file or attach them 27 2 Why am I having issues during the creation of the Landsat virtual raster The automatic creation of the virtual raster after Landsat conversion to reflectance is not required for the classi fication Errors could happen if the output destination path contains special characters such as accented letters or spaces try to rename directories e g rename new directory to new directory If you still get the same error you can create a virtual raster manually 27 3 Error 26 The version of Numpy is outdated Why QGIS 32bit could have an older version of Numpy as default in order to update Numpy 1 download this file which is based on WinPython installer and PyParsing 2 extract the file with 7 zip 3 copy the content of the extracted directory inside the directory apps Python27 Lib site packages inside the QGIS installation directory e g C Program Files x86 QGIS Chugiak apps Python27 Lib site packages overwriting the files pyparsing numpy matplotlib and scipy 208 Chapter 27 Errors Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Alternatively you should be able to install QGIS and Numpy with the OSGEO4W advanced installer 27 4 Error Plugin is damaged Python said ascii Why It c
6. And the resulting land surface reflectance is given by p n Ly Lp d ESU Ny x cos0s ESUN W m2 um values for Landsat sensors are provided in the following table Band Landsat 4 Landsat 5 Landsat 7 1 1957 1983 1997 2 1825 1769 1812 3 1557 1536 1533 4 1033 1031 1039 5 214 9 220 230 8 7 80 72 83 44 84 90 from Chander amp Markham 2003 from Finn et al 2012 For Landsat 8 ESUN can be calculated as from http grass osgeo org grass65 manuals i landsat toar html ESUN r x d RADIANCE MAXIMUM REFLECTANCE MAXIMUM where RADIANCE_MAXIMUM and REFLECTANCE_MAXIMUM are provided by image metadata An example of comparison of to TOA reflectance DOS1 corrected reflectance and the Landsat Surface Reflectance High Level Data Products ground truth is provided in Figure Spectral signatures of a built up pixel page 49 0 3 0 25 0 2 0 15 mil TOA DOS 1 sp Surface Reflectance Value 0 1 0 4 0 8 1 1 2 1 4 1 6 1 8 2 22 24 Wavelength um Fig 8 1 Spectral signatures of a built up pixel Comparison of TOA reflectance DOS1 corrected reflectanc Reflectance High Level Data Products and Landsat Surface References Chander G amp Markham B 2003 Revised Landsat 5 TM radiometric calibration procedures and postcali bration dynamic ranges Geoscience and Remote Sensing IEEE Transactions on 41 2674 2677 8 4 DOS1
7. If you found a plugin error please read How can I report an error page 207 Log file e Records events in a log file Q start recording events in a Log file Export Log file open a window for choosing where to save the Log file i e a txt file Clear Log file content clear the content of Log file Test e Test dependencies test SCP dependencies GDAL GDAL subprocess NumPy SciPy Mat plotlib Internet connection a window displays the test results 132 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 E a Semi Automatic Classification Plugin ot D 2 c 8 zn pecus F NY CD Settings 332 n euni m ES E 7 1 Fig 17 20 Settings Debug 17 6 Settings 133 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 134 Chapter 17 Main Interface Window CHAPTER 18 Spectral Signature Plot The Spectral Signature Plot window allows for the displaying of spectral signature plots which are the signature values usually reflectance as a function of wavelength Signatures can be added to the Spectral Signature Plot through the ROI Creation dock page 89 and the Classification dock page 95 Only sig natures checked in the Plot Signature list page 136 are displayed which is independent from Signature list page 95 in the Classification
8. It is possible to easily translate the user manual to any language because it is written in reStructuredText as markup language using Sphinx Therefore your contribution is fundamental for the translation of the manual to your language The following guide illustrates the main steps for the translation which can be performed using the free online service Transifex using the gettext po files Method 1 Translation using the free online service Transifex This is probably the easiest way to translate the manual using an online service 1 Transifex free registration Go to the Transifex login page https www transifex com signin You can sign in using your Google or Facebook account or with a free registration 2 Join the Semi automatic Classification Manual project Go to the page https www transifex com semi automatic classification semi automatic classification plugin 4 manual Select your language and click the button Join team If your language is not listed click the button Request language 3 Translation There are several files to be translated which refer to the sections of the SCP manual The translation is performed through an online application which shows you each sentence in the original English version and a text editor allows for the translation to your language This should make the translation process very rapid and easy Method 2 Translation using the gettext po files In order to use this meth
9. Redo create a new classification preview centred at the same pixel of the previous one activate the pointer for the creation of a classification preview left click the map for starting the clas sification process and showing the classification preview right click for starting the classification process and showing the algorithm raster of the preview 98 Chapter 16 Classification dock Semi Automatic Classification Plugin Documentation Release 4 8 0 1 e zoom to the last temporary preview in the map e lt Show gt show hide the temporary preview in the map Transparency change temporary preview transparency on the fly which is useful for comparing the results to input image 16 5 Classification style E Select qml Tu set Fig 16 6 Classification style Class colors for classifications and previews are defined in the Signature list page 95 in addition a classifi cation style can be loaded from a QGIS qml file saved previously e Select qml P select a qml file overriding the colors defined in the Signature list page 95 Reset reset style to default i e class colors are derived from the Signature list page 95 16 6 Classification output Classification output Apply mask Reset _ Create vector rn Classification report Ferform classification Fig 16 7 Classification output The classification output is a tif raster file
10. 0 0210248 0 0953275 Y IF dip Rr Lc81910312015006L GNO0 Mones 0 118014 Y IF clip RT LC81910312015006LGN00 W 0 02 30609 0 345555 Y WW clip RT LC81910312015006LGNO00 W o 0160243 0 261274 v B clip RT Lc81910312015006LGNO00 B 0 01 20209 0 178342 v Bl study area Frascati m NDM v ue CB Mio g 1 fmacrociass2 ES b WkWs lo CO TA E E 3121274657104 ERUEDNEO 0 Fig 11 10 Clipped Landsat bands e 9 Semi Automatic Classification Plugin ORO 6 LEF Tools af Pre processing Eb Post processing Band cale mm Band set X Settings P About E ES raster bands i clip_RT_LC81910312015006LGN00_B2 tif ANDE iv clip_RT_LC81910312015006LGN00_B3 tif BH Select all iv clip RT LC81910312015006LGN00 B4 tif vi E Add rasters to set J Y clip RT LC81910312015006LGN00 B5 tif IA LL clin RT 1 c910102120150n61 GNAN RA tif clip_RT_LC81910312015006LGN00_B2 tif 0 48 clip_RT_LC81910312015006LGN00_B3 tif 0 56 clip_RT_LC81910312015006LGN00_B4 tif 0 655 clip RT LC81910312015006LGN00 B5 tif clip RT LC81910312015006LGNO0 B6 tif clip RT LC81910312015006LGN00 B7 tif Fig 11 11 Definition of a band set 72 Chapter 11 Tutorial 2 Land Cover Classification of Landsat Images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Project Edit View Layer Settings Plugins Vector Raster Database SCP Hel
11. From the main menu select Plugins Manage and Install Plugins Project Edit View Layer Settings Plugns Vector Raster Database Web Processing Help Manage and Install Plugins o a e a e B 9 Vie a F be za a ie S ey DeB BLE Te From the menu A11 select the Semi Automatic Classification Plugin and click the button Install plugin 11 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Semi Automatic Classification Plugin Plugin for the semi automatic supervised classification designed to expedite the ig of multispectral or hyperspectral remote sensing images which provides a set of tools for pre processing and post processing Written by Luca Congedo the Semi Automatic Classification Plugin SCP lea fee for the semi automatic supervised classification of remote sensing eames Providing tools to expedite the creation of ROIs training areas inu rene region growing or multiple ROI creation The spectral me of training areas ca automat id di 1 isplayed in a spedral n signature plot It is possible to import sp signatures from eem T sources Also a tool allows for the selection and download of spere TE Signatures E the USGS Spectral Library http spedab cr usgs gov spectral lib html Several tools are available for iM 20m andbedeckung Classificazione della Copertura ra del Suolo Forn more e formation please vi romolstor blogspot com Yee er Ww 25 ra
12. Pleiades QuickBird RapidEye Sentinel 2 SPOT 4 SPOT 5 SPOT 6 WorldView 2 WorldView 3 e Create virtual raster of band set create a virtual raster of bands Create raster of band set stack bands stack all the bands and create a unique tif raster Build band overviews build raster overviews i e pyramids for improving display performance 17 6 Settings The tab Settings allows for the customization of SCP settings 17 6 1 Settings Interface Customization of the interface Field names of training shapefile Set the names of fields in the Training shapefile page 89 Changing field names according to the fields of an existing shapefile is effective for using external shapefiles as Training shapefile page 89 see this video e MC ID field Q name of the Macroclass ID field default is MC ID e MC Info field Q name of the Macroclass Information field default is MC info e C ID field Q name of the Class ID field default is C ID e C Info field Q name of the Class Information field default is C info Reset field names reset field names to default ROI style Change ROI colour and transparency for a better visualization of temporary ROIs on the map e Change colour Q change ROI colour Reset ROI style reset ROI colour and transparency to default Transparency Q change ROI transparency Spectral signature Plot le
13. Tutorials page 205 Why using only Landsat 8 band 10 in the estimation of surface temperature page 205 Errors page 207 How can I report an error page 207 Why am I having issues during the creation of the Landsat virtual raster page 208 Error 26 The version of Numpy is outdated Why page 208 Error Plugin is damaged Python said ascii Why page 209 Other page 211 What are free and valuable resources about remote sensing and GIS page 211 Where can I ask a new question page 211 Where can I find more tutorials about SCP also in languages other than English page 211 How can I translate this user manual to another language page 212 This is a collection of Frequently Asked Questions For other questions please visit From GIS to Remote Sensing FAQ 199 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 200 CHAPTER 24 Plugin installation 24 1 How to install the plugin manually In order to install the plugin manually 1 download the zip file from the QGIS Python Plugins Repository 2 open the directory agis2 python plugins that is inside the User Home directory and delete the folder SemiAutomaticClassificationPlugin if present 3 extract the downloaded zip file inside the directory qgis2 python plugins 4 the plugin should be installed start QGIS open the Plugin Manager and be sure that Semi Automatic Class
14. 1993 n 0 z y cos Liza TM Qoa cm ia 92 Where e x spectral signature vector of an image pixel e y spectral signature vector of a training area e n number of image bands Spectral angle goes from O when signatures are identical to 90 when signatures are completely different 7 6 3 Euclidean Distance The Euclidean Distance is particularly useful for the evaluating the result of Minimum Distance page 40 classi fications In fact the distance is defined as where x first spectral signature vector y second spectral signature vector e n number of image bands The Euclidean Distance is 0 when signatures are identical and tends to increase according to the spectral distance of signatures 7 6 4 Bray Curtis Similarity The Bray Curtis Similarity is a statistic used for assessing the relationship between two samples read this It is useful in general for assessing the similarity of spectral signatures and Bray Curtis Similarity S x y is calculated as Ma Yi S x y 100 E 100 where x first spectral signature vector e y second spectral signature vector e n number of image bands The Bray Curtis similarity is calculated as percentage and ranges from O when signatures are completely different to 100 when spectral signatures are identical 7 6 Spectral Distance 43 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 7 7 Classific
15. Cinfo Color MCID MCifo itv C Info A P x t s 2 amp g be cc j EH LEE i i export import Add to signature IAS E 925 s Y m See Clesen algorithm ry Range radius Min ROI size Max ROI width df Maximum Likelihood 0 0000 4 0 006000 60 100 9 z 2 v Use Macroclass ID v Rapid ROI on band 1 mm lassification preview Automatic refresh ROI Automatic plot a Sel 500 Redo y o 4B s Show Transparency Redos J Show D c sp IE Select qmi Reset o A oem ROI Signature definition Aa Apply mask Reset 0 4 Unclassified T create vector _ Classification report co Cinfo Perform classification 24 A Clouds Save ROI v Add sig list SCP Classification Layers Coordinate 710952 1150476 Scale 1 1 451 348 v Rotation 0 0 v Render EPSG 32616 Fig 20 20 Definition of SCP input for the Landsat image LC80160532014057LGN00 a Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help BE S E FEEL 0 IIS 55 s 9A AS PIE REA M fe 6 ye SCP Classification x SCP ROI creation y G 1 Open Save Reset ROI v Lo Newshp V og Emam T M S ICIv MCInfo CID Cinfo Color MCID MCinfo itv C info e Fat lo ud s o 1 1 Built up 1 Buitupl E 2 v 1 Bult Built 243 Soil 2 Soil a 3 x2 Veg 3 Forest 3 2 Vegetation 3 Forest amp e NO v 2 veg 4 Fore 4 2 Vegetation 4 Fore
16. Spectral angle 34 0624867717 Euclidean distance 0 216964146836 Bray Curtis similarity 96 62 7067211142 Jeffries Matusita distance Spectral angle Euclidean distance Bray Curtis similarity 96 f MC ID 1 MC info Built up C ID 1 C_info Built up1 MC_ID 3 MC_info Soil C_ID 26 C_info Soil7 1 98274155418 4 46039250176 0 118688900614 87 9022037659 MC ID 1 MC info Built up C ID 1 C info Built upl MC ID 4 MC info Water C ID 31 C info Water7 Jeffries Matusita distance 1 99993088463 Spectral angle Euclidean distance Bray Curtis similarity 96 Project Edit View Layer 8 B amp B SCP Classification Settings Plugins Vector QE AUN o x 33 0394038488 0 285476907426 46 2720393743 o x Color _ Plot a Remove signatures Fit plot to data WV Calculate spectral distances p Ny SCP Spectral Signature Plot S ICIv MC Info CID C Info CAR Built up Built up1 272 Vegetation 2 Trees a v3 Soil 26 Soil7 4 v4 Water 31 Water7 Plot Signature details Spectral distances Q Close Fig 20 13 Spectral distances Raster Database Web SCP Processing Help lt lt band set gt gt Y E m x n acm o i 8e M 8e SCP ROI creation Vo FF open Save Reset ROL Y JLo new shp Ve z S ICIv MC Info CID Cinfo MCD MCinfo Kv C Info 9 A 7 x1 Bui
17. The Semi Automatic OS is available as a 32 bit and 64 bit virtual machine that can be run in the open source Vir tualBox or any other virtualization program The following is a guide for the installation of the Semi Automatic OS in the open source program of virtualization VirtualBox 191 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 192 CHAPTER 23 Installation in VirtualBox Download VirtualBox open source software select a proper version depending on your OS and install it at the end of the installation restart the system Download the Semi Automatic OS virtual machine about 600 MB from here 32 bit or 64 bit Extract the virtual machine content in a directory it requires about 3 GB of disk space the file is com pressed in 7z format if needed download the open source extraction software from http www 7 zip org 4 Run VirtualBox and create a new Debian virtual machine a Click the New button b Type a name for the virtual machine for instance Semi Automatic OS select Linux and Debian 32 _ or 64 bit as Type Version respectively click Next S Oracle VM VirtualBox Manager File Machine Help ti Details Snapshots t Star cad 5 Deti New Settings Start Disco omm Name and operating system ial machines on your Please choose a descriptive name for the new virtual haven t created any nn gt machine and select the type of operating system you wa inte
18. Metadata for the corresponding bands are automatically filled using the metafile found inside the Directory containing Landsat bands or defined in Select MTL file In addition it is possible to edit the metadata manually For information about metadata fields read this page and this one Satellite gt satellite name e g Landsat8 Date date acquired e g 2013 04 15 e Sun elevation gt Sun elevation in degrees Earth sun distance gt Earth sun distance in astronomical units automatically calculated if Date is filled Remove band remove highlighted bands from the table list Table fields RADIANCE MULT multiplicative rescaling factor RADIANCE ADD additive rescaling factor REFLECTANCE MULT multiplicative rescaling factor REFLECTANCE ADD additive rescaling factor RADIANCE MAXIMUM radiance maximum REFLECTANCE MAXIMUM reflectance maximum K1 CONSTANT thermal conversion constant 17 2 Pre processing 115 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 K2_CONSTANT thermal conversion constant LMAX spectral radiance that is scaled to QCALMAX LMIN spectral radiance that is scaled to QCALMIN QCALMAX minimum quantized calibrated pixel value QCALMIN maximum quantized calibrated pixel value Create Virtual Raster if checked a virtual raster named 1and
19. a tool allows for the selection and download of spectral signatures from the USGS Spectral Library http spedab cr usgs gov spectral lib html Several tools are available for the pre processing phase image dipping Landsat conversion to reflectance the classification process Minimum Distance Maximum Likelihood Spectral Angle Mapping algorithms and classification previews Bb search 8 format ePS CRS Pugn and the post processing phase conversion to vector accuracy assessment land cover change classification d selecta report This plugin requires the installation of GDAL OGR Numpy SciPy and Matplotiib Also a virtual machine is available http fromgistors blogspot com p semi automatic os html Keywords EJEM 28 525 HERA AERA ol VI platil Lanas Classifica o da Cobertura do Solo Clasificaci n de la Cobertura de la Tierra Classification de la Couverture du Sol knaccubwxaus emnenonezosanna Klassifizierung der Landbedeckung Classificazione della Copertura del Suolo For more information please visit http fromgistors blogspot com BD SENSUM Earth Observation Tools f 173737 7 fr 25 rating vote s 38811 downloads Wi sc Diagram Downloader Me stepene Tags raster landsat spectral signature classification land cover accuracy scatter plot supervised Spot classification dos 1 clip remote sensing mask analysis land cover change Shell More info homepage tracker code repository Shortcut Manager shptoobs BE
20. and for the creation of ROIs Regions Of Interest using a region growing algorithm or manual drawing The training shapefile created with SCP is used for storing the ROI polygons SCP allows for the creation of temporary ROI polygons using a region growing algorithm i e image is seg mented around a pixel seed including spectrally homogeneous pixels Alternatively ROIs can be drawn manually Temporary ROIs are displayed over the image If the ROI characteristics e g spectral signature are considered acceptable then it can be saved in the training shapefile and the spectral characteristics are saved in the Signature list file page 95 It is worth pointing out that classification is not based on ROIs but it is based on the spectral characteristics of signatures in the Signature list page 95 ROIs are defined with a Macroclass ID and Class ID see Classes and Macroclasses page 39 that are used for the classification process in addition Macroclass Information e g macroclass name and Class Information e g class name can be defined but they are not used in the classification process Training shapefiles which can be created by SCP must contain at least four fields as in the following table custom names can be defined in the Field names of training shapefile page 129 Description Field name Field type Macroclass ID MC_ID int Macroclass Information MC_info string Class ID C_ID int Class Informati
21. e E o et e PP dassification 1 NDVI 10 0 Unclassified Vi 1 Built up 9 2 Vegetation Add to signature ba L n b 25 3 Sol 2d 4 Water NS Range radius Min ROI size Max ROI width G 0 010000 gt 60 100 x _ Rapid ROI on band 1 te Automatic refresh ROI Automatic plot a J Show O y Display cursor for NDVI v Hm a lis Add sig list SCP Classification Layers SH e 5 coordinate 920635 1113580 Scale 1 1 447 598 v Rotation 0 0 v Render QEPSG 32616 Fig 20 28 Classification 1 with masked clouds and have high reflectance values in the blue band Landsat 7 is also affected by black stripes i e SLC off that we are going to mask as well We are going to create an expression that identifies pixel values below a certain temperature threshold for the Thermal band band 6 for Landsat 7 and above a certain reflectance threshold for the Blue band band 1 In QGIS load all the Landsat bands inside the directory LE70150532014090EDC00 converted Use the following expression in the Band calc page 125 np where RT LE70150532014090EDCOO B6 VCID 1 23 amp RT_LE70150532014090EDC00_B1 The first part RT LE70150532014090EDCOO B6 VCID 1 23 RT LE70150532014090EDCOO B1 20 1 means that we are going to mask pixels that have both temperature lower than 23 C and Blue band reflectance greater than 0 1 These threshold values have been identified
22. 0 793 Add to signature PAL A E 25 Wf RT_LC80150532014050LGN00_B4 AOI DANN a MS me Range radius Min ROI sze Max ROI width 7 4 A Wf RT 1c80150532014050LGN00 83 0 006000 60 100 zt H Mo Y Rapid ROI on band mn vi 0 659 D Wf RT 1c80150532014050LGN00 82 Automatic refresh ROI Automatic plot a E n a y 3 0 65 SE f Redos J Show O E Y Display cursor for NDV v f A gt R01 Signature definition ____ aaa FG MCID MC info FA 0 4 Unclassified o tid C info gt e 24 I Clouds SCP Classification Layers Save ROI Y Add sig list Undo 1 legend entries removed 5 coordinate 9361921 1109891 Scale 1 37 941 v Rotation 0 0 v Render EPSG 3857 OTF Fig 20 18 OpenStreetMap loaded in QGIS 164 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 classification_1 tif Project Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help eM lt lt bors set gt gt vos ER 532 vo stow A A DS FA El ARO fie i fle a V SCP Classification 6x SCP ROI creation ox pi o anature list file Training shapefile m Open Save Reset ROI vj g Newshp E signaturelist 00 Co E S ICIvMCinfo CID Cinfo Color MCID MCinfo ZIlw C info A lv Uncl 7 Clou EN Built up 1 Built up1 I EN v1 Built 16 Built EN 2 Vegetation 2 Trees c 3 v
23. 06BBGRIOSSSSa M dd Jd Ra BY Ep Ed amp v CO 3 jv E 5 j J X a e abe E an 5 ate Layers ox 06GIS Y Y Y AMM amp E ay VID 4s 0 207 0 915 Scale 1 1547662 v 4 2 Semi Automatic Classification Plugin installation Run QGIS 2 From the main menu select Plugins gt Manage and Install Plugins 19 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Project Edit View Layer Settings Plugins Vector Raster Database Processing Help Manage and Install Plugins Python Console From the menu A11 select the Semi Automatic Classification Plugin and click the button Install plugin Ehe Semi Automatic Classification Plugin Not instal Plugin for the semi automatic supervised classification designed to expedite the rare processing of multispectral or hyperspectral remote sensing images which provides a x MUN set of tools for pre processing and post processing ud Written by Luca Congedo the Semi Automatic Classification Plugin SCP allows for the semi automatic supervised classification of remote sensing images providing tools to expedite the creation of ROIs training areas through region growing or multiple ROI creation The spectral signatures of training areas can be automatically calculated and displayed in a spectral signature plot It is possible to import spectral signatures from external sources Also
24. 1 55 1 75 30 Band 6 Thermal Infrared 10 40 12 50 120 resampled to 30 Band 7 SWIR 2 08 2 35 30 The resolutions of Landsat 7 sensor are reported in the following table from http landsat usgs gov band_designations_landsat_satellites php days NASA 2013 also Landsat temporal resolution is 16 Landsat 7 Bands Wavelength micrometers Resolution meters Band 1 Blue 0 45 0 52 30 Band 2 Green 0 52 0 60 30 Band 3 Red 0 63 0 69 30 Band 4 Near Infrared NIR 0 77 0 90 30 Band 5 SWIR 1 57 1 75 30 Band 6 Thermal Infrared 10 40 12 50 60 resampled to 30 Band 7 SWIR 2 09 2 35 30 Band 8 Panchromatic 0 52 0 90 15 The resolutions of Landsat 8 sensor are reported in the following table from http landsat usgs gov band_designations_landsat_satellites php days NASA 2013 also Landsat temporal resolution is 16 Landsat 8 Bands Wavelength micrometers Resolution meters Band 1 Coastal aerosol 0 43 0 45 30 Band 2 Blue 0 45 0 51 30 Band 3 Green 0 53 0 59 30 Band 4 Red 0 64 0 67 30 Band 5 Near Infrared NIR 0 85 0 88 30 Band 6 SWIR 1 1 57 1 65 30 Band 7 SWIR 2 2 11 2 29 30 Band 8 Panchromatic 0 50 0 68 15 Band 9 Cirrus 1 36 1 38 30 Band 10 Thermal Infrared TIRS 1 10 60 11 19 100 resampled to 30 Band 11 Thermal Infrared TIRS 2 11 50 12 51 100 resa
25. Casken de la Couverture du Sol knaccupukauna sennenonb3oBaHna Klassifizierung der Landbedeckung Classificazione della Copertura del Suolo For more information please E Mf Semi Automatic Cassficaton Pl EE visit http ifiromgietors blogspot com di sencace Y PD SENSUM Earth Observation Tools Vee rfr 25 rating vote s 38811 downloads SG Diagram Downloader m Category Raster z Shapefie Encoding Fixer Tags Raster Classification Land Cover Remote Sensing Analysis Landsat Land Cover er Change Accuracy i de see LSunervised classification Snertral sionature Mack Scatter lot Clin DC E Be sort menager r ograde ali Urinstal plug Renstal pugn In RAM page 132 set the available RAM in MB for processing entering half of the system RAM for instance it your system has 2GB of RAM enter 1024 If the system is 32bit due to system limitations you should not enter values higher than 512MB In order to ease the photo interpretation in the following steps we are going to use also the OpenLayers Plugin which allows for the display of several maps If you don t have already installed follow the same steps previously described and install the OpenLayers Plugin in QGIS 20 2 Download and Pre processing of Landsat images We are going to download Landsat 7 and 8 images using the SCP tool Download Landsat page 108 Landsat images are available from the U S Geological Survey and these bands are downloaded through the
26. Chapter 7 Supervised Classification Definitions Semi Automatic Classification Plugin Documentation Release 4 8 0 1 e n number of image bands Therefore the distance is calculated for every pixel in the image assigning the class of the spectral signature that is closer according to the following discriminant function adapted from Richards and Jia 2006 EC lt d z yk lt d x yj Vk Ag where C land cover class k Yp spectral signature of class k yj spectral signature of class j It is possible to define a threshold T in order to exclude pixels below this value from the classification x E Ck gt d x yk lt d z yj Vk A j and d x yx lt Ti 7 5 2 Maximum Likelihood Maximum Likelihood algorithm calculates the probability distributions for the classes related to Bayes theorem estimating if a pixel belongs to a land cover class In particular the probability distributions for the classes are assumed the of form of multivariate normal models Richards amp Jia 2006 In order to use this algorithm a sufficient number of pixels is required for each training area allowing for the calculation of the covariance matrix The discriminant function described by Richards and Jia 2006 is calculated for every pixel as 1 1 gk x Inp Cx 5 P t Ya E yn where C land cover class k x spectral signature vector of a image pixel p Ck probability that the correct cl
27. Classification 10 1 Data sueo la SUE YR RIS REUS A ums 1020040 Data octets dos ae ks Sat DOE xe A s Att anes Eh Een 10 3 Setithe Input Image im SCP sn 463 arras a be SORS 10 4 Create the Training Shapefile and Signature List File llle 10 5 Create the ROIS uc ok Gue soma a a Sk Sedo wg e E ERES EORR G 10 6 Create a Classification Preview 3 4 5 som sm ok e a al ea 10 7 Create the Classimcation Output uz ee uoo a ecg on Be Be eae TRES ER a REL Rd 11 Tutorial 2 Land Cover Classification of Landsat Images Tii Data Downloade s a e EA eh a A Soe ee A 11 2 Automatic Conversion to Surface Reflectance 2 22e 11 37 Chip Data s acne ee os dom be v Eng RS pO SRE eR Ee S BOR ORE ERS 11 4 Createthe Band Set ook ok da a4 54 tos SO IE Rw RO ee d 11 5 Open the Training Shapefile and Signature List File less 11 6 Create the ROIS ws ea A ee da AE ORS Ee ERR e 11 7 Create a Classification Preview ue eS GS RG ek RUE Oe OR Re BUR RO 11 8 Assess Spectral Sipnatures i es x no on GA a eee ee ea be 11 9 Create the Classification Output i cs s mem oe Re bo ee a ae we E 12 Other Tutorials IV The Interface of SCP 13 SCP menu 14 Toolbar 15 ROI Creation dock 15 1 Pramunpshapefle lt 22e c Rm m B eh be ee e ee ee EA 15 2 ROLIS 4v ous poe bete feeder gd at ep Ge eR el e eccl o i Deos 15 3 ROL parameters i oe REOR eoe ec esc RARE xo om Yoda ee one dei te es Ge al P RH 15 4 ROFPGEIGaU0BD vou
28. Fig 11 9 The tool for clipping the bands with the shapefile When the process is completed clipped rasters are loaded in QGIS We can remove the original Landsat bands from QGIS 11 4 Create the Band Set Now we need to define the Band set which is the input image for SCP Open the tab Band set page 127 clicking the button in the SCP menu page 85 or the Toolbar page 87 Click the button Select A11 then Add rasters to set order the band names in ascending order from top to bottom using the arrow buttons Finally select Landsat 8 OLI from the combo box Quick wavelength settings in order to set automatically the center wavelength of each band this is required for the spectral signature calculation You can notice that the item lt lt band set gt gt isselectedas Input image in the Toolbar page 87 11 5 Open the Training Shapefile and Signature List File We are going to open the Training Shapefileand Signature list file already created in Tutorial 1 Your First Land Cover Classification page 57 If you don t have these files follow the instructions Create the Training Shapefile and Signature List File page 58 Load in QGIS the Training shapefile saved previously e g ROI shp from the QGIS menu Layer gt Add Vector Layer The shapefile is displayed in QGIS 11 4 Create the Band Set 71 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 0 0799775 clip_RT_LC81910312015006LGNO0O
29. Frascati New shp QR Y F AT_1819103120150061 GNo0_87 d Q MCID Meino CID C info e 6 v v IW Rr LC81910312015006LGNO0 B6 A gt E EE IF RT LC81910312015006LGN00_B5 3 Mo 436 v v I RT LC81910312015006LGN00 B4 Yo 9 32 L a G IM RT LC81910312015006LGNO0 B3 EE oti Ve Mo Max ROI width a 0 284 100 BP RT_LC81910312015006LGN00_B2 za E E 1 Rapid ROI on band 1 Nw _ Automatic refresh ROI Automatic plot a NS Beso 1 8 fu y Display cursor for NOV v e Show ROI E MCID MC Info j 1 9 Macroclass 1 ES EZ a cio IC info 1 o Class 1 zd Layers SCP Classification Y Add sig list gt coordinate 327193 4514654 Scale 1 1 537 828 v Rotation o c v Render EPSG 32633 Y Fig 11 8 The study area shapefile 70 Chapter 11 Tutorial 2 Land Cover Classification of Landsat Images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 to see the shapefile in the list Click the button Clip selected rasters and select a directory e g Landsat_clip where clipped bands are saved with the file name prefix clip_ Fo Semi Automatic Classification Plugin oo w ees Bp Postprocessing Band eale mm Bandset A Settings 7 About RT LC8191031201 5006LGNO0 B6 v RT LC81910312015006LGN00 B5 Y RT LC81910312015006LGN00 B4 i RTICRTa103120n15nn6I GNAN R3 Clip coordinate s BE x l e OC GNNNSDSSSSSSENED
30. Google Earth Engine and the Amazon Web Services Also we are going to convert Landsat images to reflectance and apply the DOS1 atmospheric correction see Landsat image conversion to reflectance and DOSI atmospheric correction page 47 First we need to download the Landsat database in SCP Open the tab Download Landsat page 108 clicking the button be in the SCP menu page 85 or the Toolbar page 87 Click the button Select database directory in order to define where to save the database It is preferable to create a new directory e g LandsatDB in the user directory Click the button Update database and click Yes in the following ques tion about updating the image database TIP Landsat databases are updated daily therefore when you need up to date images you should click the button Update database in order to the get the latest Landsat images Now we could define the Area coordinates page 110 of the study area click Find images and browse Landsat images Each Landsat image has a unique ID i e identifier In this tutorial we are going to use two Landsat 8 images acquired on February 2014 IDs LC80150532014050LGNO00 and LC80160532014057LGNO0 and a Landsat 7 image acquired on March 2014 ID LE70150532014090EDC00 of course more images are required for the classification of the whole Country 150 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Docum
31. HORROR TRU AES 31 62 Remote Sensing definitions 2 444 a A SUUS SS 31 6 9 JSensOISo oc poe AAA 33 64 Radiance and Retlectance 4 uode SUR ESSE SA pH eg Sede eed 33 6 5 Spectral Signature 2 54 2044 2a eR PS PO SOR ROW so UR ox OS S ROS XC A OR P VOSTRO 33 6 6 Landsatsatelllte cuco eaii pa e e a e E o vehi e dus 34 6 7 Sentinele2 Satellite uus e A Ee a RR RA 34 6 8 Color COMPOST cesar e a bale 35 6 9 JPan2sharpemime i 3g pce A ra BA Be he d o 35 Supervised Classification Definitions 39 TA Land Cover 2 2o aota p rs a de e oe E ee ew a 39 7 2 Supervised Classification s s uuo e ake Sb oh a SE ae a a A 7 3 Training Areas 2 Los bs eke ke oret A we ea e ee So TA Classes and Macroclasses 4 ok e ee a Se ee ee e Ta Classification Algorithms oes ex 9 AE ROE P BO A HO pectral DIStal6e ios e A E AA AAA E ER Ai 7 1 Classification Result zuo a ROR we ad RS 7 9 Accuracy Assessment 2 556 ic A SOR A EUR S E E ARE A Run 8 Landsat image conversion to reflectance and DOS1 atmospheric correction 8 1 Radiance atthe Sensors Aperture nos v RR RR RR RR RR a a sa ae aS 8 2 Top Of Atmosphere TOA Reflectance oo coc cuaca o o m oy RR o m ko o Rn 8 3 Surface Rellectance x uoo hoe Rue eee E XE Rap Ro Be cede p De ud n ay 8 4 JDOST CorrectiOB eos xor Rem mo USO RUE E YU E ede Ue x Ros e EIE E Ewe RU 9 Conversion to At Satellite Brightness Temperature III Basic Tutorials 10 Tutorial 1 Your First Land Cover
32. Signatures page 78 Create the Classification Output page 79 Other Tutorials page 81 55 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 56 CHAPTER 10 Tutorial 1 Your First Land Cover Classification This is a basic tutorial about the use of the Semi Automatic Classification Plugin SCP for the classification of a generic image It is worth noticing that the image is multi spectral It is recommended to read the Brief Introduction to Remote Sensing page 29 before this tutorial In this tutorial we are going to classify a remote sensing image acquired over Frascati Rome Italy in order to identify the following land cover classes 1 Water 2 Built up 3 Vegetation 4 Bare soil Following the video of this tutorial http www youtube com watch v nZffzX_sMnk Alternative video link https archive org details video_basic_tutorial_1 10 1 Data Download the image data from here data available from the U S Geological Survey Itis a Landsat image but in this tutorial we are going to use this raster as a generic dataset For a specific tutorial about Landsat images read Tutorial 2 Land Cover Classification of Landsat Images page 65 Unzip the downloaded file in a directory of your choice The dataset is a multi spectral raster the file sample_image tif that includes the following Landsat bands 1 Blue Green Red Near Infrared Short Wavelength I
33. Vegetation ROI deciduous trees 11 6 Create the ROls 77 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 RGB 3 2 1 RGB 4 3 2 RGB 3 4 6 Fig 11 23 Vegetation ROI crop 11 7 Create a Classification Preview As pointed out in Tutorial 1 Your First Land Cover Classification page 57 previews are temporary classifica tions that are useful for assessing the effects of spectral signatures during the ROI collection Set the colors of the spectral signatures in the Signature list page 95 then in the Classification algorithm page 97 select the classification algorithm Spectral Angle Mapping In Classification preview page 98 set Size 500 click the button and then left click the map in order to create a classification preview The preview result is displayed in the map Previews are temporary rasters deleted after QGIS is closed placed in a group named Class_temp_group in the QGIS panel Layers El gt BF dip KT Lcm 9103120150061 GNO0 SP ABaAINARS OF Place the Class_temp_group to the top of layers in order to display the preview over the image Also in Classification preview page 98 switch the button Show in order to show or hide the previews In QGIS you could notice one or more warnings similar to this Warning 9 The following signature has wavelength different from band set Macro 1 ID 1 see the fol lowing Figure Warning 9 page 79 This is because in Open th
34. a P e We Mieco Cre A Al e E e a CUM ie Ya y Sy A Fig 21 6 The NDVI calculated 21 3 Classification refinement basing on NDVI values Load the downloaded classification in QGIS E u enge Phigns vector Raster Database web sce Processing Hep i TTE Jo MEE ca PER ARS Lielies te6 B vs SCP RO creation y np o 13 p 9 n gJ Jet E W g Adaro mgnature mi Range radius Min ROI sce Ma ROI width 010000 60 6 100 WERPESSYN4NS O oO may Automaterdresh ROI _ tomate pot VE Pro creston 8 I show not Coordnate Sirosssereies Sa esis o 0 0 y Render EPSG32533 Fig 21 7 The land cover classification The classification is the result of Tutorial 2 Land Cover Classification of Landsat Images page 65 where the land cover classes described in the following table were identified Class name Pixel value Water 1 Built up 2 Vegetation 3 Bare soil 4 We are going to refine this classification defining the following condition pixels having NDVI gt 0 5 are classified Vegetation The value 0 5 is an arbitrary value that should be changed according to the image condition i e phenological state of vegetation Open the Band calc page 125 and click the button Refresh list Clear the content of Expression and write the following expression np where NDVI tif gt
35. about updating the image database 65 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Update image database Are you sure you want to download the Landsat image database requires internet connection Fig 11 1 Download Landsat 8 database The download should start about 7 MB When the download is completed in the search box Image ID paste the Landsat ID 1C81910312015006LGN00 Now click the button Find images and after a few seconds the image will be listed in the Image list Click the tab Download options and leave checked only bands from 2 to 7 we don t need the other bands for this tutorial Also uncheck all the options only if preview in Layers Pre process images and Load bands in QGIS we are going to see these functions in other tutorials In order to start the image download click the button Download images from list and select a directory where bands are saved i e Desktop The download could last a few minutes according to your internet connec tion speed each Landsat band is about 50MB The progress bar inform you about the downloading process After the download all the bands and the metadata file are saved in a new directory LC81910312015006LGN00 i e the Landsat ID created automatically 11 2 Automatic Conversion to Surface Reflectance The metadata file contains information that is useful for the automatic conversion of bands to Radiance and Reflectance page 33 Read L
36. available for e scosumer the pre processing phase image clipping Landsat conversion to reflectance the classification process Minimum Distance Maximum Likelihood Spectral Angle Mapping algorithms and classification previews IBY search a format E56 ons Pug and the post processing phase conversion to vector accuracy assessment land cover change classification Dee secre report This plugin requires the installation of GDAL OGR Numpy SciPy and Matplotlib Also a virtual machine is available http fromgistors blogspot com p semi automatic os html Keywords 338528 mE rend HA BRE MERITIS o Ml pla usa Classifica o da Cobertura do Solo Clasificaci n de la selenext Cobertura de la Tierra Classification de la Couverture du Sol knaccidwkauiws sennenonesogarvs Klassifizierung der Landbedeckung Classificazione della Copertura del Suolo For more information please 3 ES Semi Automatic CassficatonPl I Visit nttp fromgistors blogspot com de senaace A D SENSUM Earth Observation Toos We Vr 11 r fr 25 rating vote s 38811 downloads SG Diagram Downioader sor Category Raster i cene col Tags Raster Classification Land Cover Remote Sensing Analysis Landsat Land Cover Change Accuracy Shelbe fa Sunervised rlassificatinn Snertral sianature Mack Scatter nlot Clin DOSI E nage ete Venetian Reale Es Lt 3 3 Configuration of the plugin Now the Semi Automatic Classification Plugin is installed and
37. click on the map for creating the ROI right click on the map for displaying the spectral signature of a pixel of the Input image Redo create a new ROI using the region growing algorithm at the same point of the previous one b create a ROI by manual drawing a polygon on the map after clicking the button left click on the map to define the ROI vertices and right click to define the last vertex closing the polygon Edad open the Multiple ROI Creation page 102 e Display cursor for gt P if the ROI creation pointer is active display vegetation index values on the map vegetation indices available in the combo box are NDVI Normalized Difference Vegetation Index and EVI Enhanced Vegetation Index NDVI requires the near infrared and red bands EVI requires 92 Chapter 15 ROI Creation dock Semi Automatic Classification Plugin Documentation Release 4 8 0 1 the blue near infrared and red bands converted to reflectance wavelengths must be defined in the Band set page 127 e lt Show gt show hide the temporary ROI in the map j zoom to the last temporary ROI 15 5 ROI Signature definition Fig 15 6 ROI Signature definition This allows for the definition of ROI s class and saving the temporary ROI to the Training shapefile page 89 in addition it is possible to automatically calculate the ROI spectral signature and add it to the Signature list page 95 MC ID P ROI Macroclass ID
38. eh Boe stes e aep ak dese BAe hed 4 tee in a dien 15 5 ROT Signature definition ss uuo ra OS RC Yo ode A Ro 16 Classification dock 16 1 Signature list file so ee Rom nO SR a det kem Swe ei RR ce e e CR 16 2 iS1pnature Hist s shee REOR RR RR A a Re ae D 16 3 Classification algorittht ss nosi eso o Rogo EA Es RR Ro m RC RUE Roh e S E E doe BE 16 4 Classification preview o s ope sooo o e e E EO Re ER 47 47 47 48 48 51 83 85 87 89 89 91 91 92 93 16 53 lassificationstyle 2o uo ae a os ca hy Rs TR BUR PRE he BOR BAR ee oy EROR 16 6 Classification output lt o ono s a o eA OR eee we eee ee doe Rd 17 Main Interface Window IED MOOI SE tex Zee is de decies quei A d di dte TA Gh etn dee t A da Be 17 2 JPr prOGESSIBB 4 Kae ey ae se v E PE a Oe SR S REY SRS 17 3 POS PrOCSSSIDO do 24 be Da hee Soe ee eo RAS PHS Se eS 17 4 Band Calle 232b bem BS RR RR RUE EE ee bh OR he ee OE ERR 175 Bandsset 15 229094 de Bae AE GRA DORE Be AA eS AS AA RE Eure Ge eR UR ec Ba UR Bae Ge UR RR ee a UR RR 18 Spectral Signature Plot I8 l PlotSignature list s s gaross s A Bw A NUR A AAA 19 Scatter Plot I9 ROLET 4o xw be bie rd Se eoo b Ee o iex Pega BS V Thematic Tutorials 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images 2001 Plusin imstallati n is as ls e ged be PXMPAOUS P EE ee ae aeq ia 20 2 Download and Pre processing of Landsat images o e 20 3 Classification of
39. enhancing the classification using the Band calc page 125 see Tutorial Using the tool Band calc page 179 In particular pixels where NDVI value is above a certain threshold will be classified as vegetation code 2 Below this NDVI threshold the Maximum Likelihood classification is un touched Of course this is an example of integration of ancillary data we could use other data such as other vegetation indices or the result of other classifications e g using Spectra Angle Mapping page 42 Now in QGIS load the bands of the Landsat 8 image LC80150532014050LGN00 and the respective land cover classification Open the Band calc page 125 and click the button Refresh list In the Band calc page 125 calculate the NDVI copying the following Expression page 126 RT LC80150532014050LGN00 B5 RT LC80150532014050LGN00 B4 RT LC801505320 L4050LGNOO B5 Click the button Calculate select where to save the NDVI e g a new file named NDVI 1 tif Then calculate the following Expression page 126 for enhancing the classification basing on the NDVI np where NDVI 1 0 6 2 classification 1 Click the button Calculate and select where to save the new classification e g classification 1 NDVI tif We can see in the following figure that the area classified as vege tation has increased In this case we have used a NDVI threshold equals to 0 6 However the threshold value has to be chosen f
40. in the image using the tool Identify of QGIS for cloud pixels in band 1 and band 6 The character means or so that the other expressions e g RT LE70150532014090EDCO00 B1 0 identify pixel values equal to 0 which are NoData for every Landsat band in order to mask the black stripes due to SLC off and the black border We could use the same method of cloud masking also for Landsat 8 images For the im age LC80150532014050LGNO0 load the bands RT LC80150532014050LGN00 B10 and RT LC80150532014050LGNO00 B2 and use the following Expression page 126 in the Band calc page 125 np where RT LC80150532014050LGNO0 B2 gt 0 03 RT LC80150532014050LGNO00 B10 lt The condition RT LC80160532014057LGN00 B2 0 allows for the masking of the image black bor der As you can see there are still gaps Unclassified pixels in the classification we would require the classification of other Landsat images in order to fill those gaps After the cloud masking of these three classifications we can create one mosaic that is the classification of the whole study area Part of the unclassified gaps has been filled with the Landsat 7 classification Of course we would require more classifications in order to fill all the gaps 20 5 Cloud Masking 171 24 RT RT Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Project Edit View Layer Settings Plugins Vector Ras
41. multi spectral images loaded in QGIS input image can be a multi spectral raster or a set of single bands defined in the Band set page 127 if the Band set page 127 is defined then this list will contain the item lt lt band set gt gt refresh image list RGB P select a color composites that is applied to the Input image new color composites can be defined typing the band numbers separated by or or e g RGB 4 3 2 or RGB 4 3 2 or RGB 4 3 2 lt Show gt show hide the input image in the map fA display the input image stretching the minimum and maximum values according to cumulative count of current extent PAM display the input image stretching the minimum and maximum values according to standard deviation of current extent pe open the Spectral Signature Plot page 135 E open the Tools page 102 87 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 L open the Pre processing page 114 gt open the Post processing page 117 open the Band calc page 125 VM open the Settings page 129 open the online user manual in a web browser gt open the Online help in a web browser also a Facebook group and a Google Community are available for sharing information and asking for help about SCP 88 Chapter 14 Toolbar CHAPTER 15 ROI Creation dock The dock ROI creation allows for the definition of a training shapefile
42. multi spectral virtual raster in QGIS 1 from the menu Raster select Miscellaneous gt Build Virtual Raster catalog 2 click the button Select and select all the Landsat bands in numerical order 3 select the output file for instance rgb vrt check Separate bands will be separated and click OK 204 Chapter 25 Pre processing CHAPTER 26 Tutorials 26 1 Why using only Landsat 8 band 10 in the estimation of surface temperature There are several methods for estimating surface temperature The method described in this tutorial requires only one band and can be applied also to Landsat 5 and 7 Moreover USGS recommends that users refrain from relying on Landsat 8 Band 11 data in quantitative analysis of the Thermal Infrared Sensor data see Changes to Thermal Infrared Sensor TIRS data by USGS 205 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 206 Chapter 26 Tutorials CHAPTER 27 Errors 27 1 How can report an error Tf you found an error of the Semi Automatic Classification Plugin please follow these steps in order to collect the required information log file 1 close QGIS if already open 2 open QGIS open the Plugin tab Settings Debug page 132 and check the checkbox Records events in a log file TNT Semi Automatic Classification Plugin Vow Log file Fig 27 1 Debug 3 click the button Test dependencies in the tab Settings Debug page 132
43. of each ROI it is useful to check the Spectral Distance page 42 in order to assess the separabil ity of ROI in fact each ROI should be different i e spectrally distant from the others in order to avoid spectral confusion and achieve better classification results In the Signature list page 95 highlight the ROIs and click the button bX Spectral signature are added to the Spectral Signature Plot page 135 1 Built up 1 Built up1 2 Vegetation 2 Trees 3 Soil 26 Soil7 4 Water 31 Water7 values 0 6 0 8 1 0 1 2 1 4 1 6 1 8 2 0 22 Wavelenath um 1 E 6m 1 Fig 20 12 Plot of spectral signatures Now click the tab Spectral distances page 138 Each table represent the Spectral Distance page 42 of each ROI combination As shown in the following figure the comparison of the Built up ROI and the Soil ROI highlights very low Spectral Angle page 43 and Euclidean Distance page 43 this means high similarity if we used the Spectra Angle Mapping page 42 or the Minimum Distance page 40 algorithms The Jeffries Matusita Distance page 42 is near 2 this means that the two ROIs are separable for the Maximum Likelihood page 41 algorithm Since we are using the Maximum Likelihood page 41 algorithm it is important that the Jeffries Matusita Distance page 42 is near 2 for each ROI combination Now we can create a classification preview see Create a Classification Preview page 61 for the basics of classi fication pre
44. page 128 Settings page 129 Settings Interface page 129 Settings Processing page 131 Settings Debug page 132 17 1 Tools The tab Tools includes several tools for the creation and manipulation of ROI and spectral signatures 17 1 1 Multiple ROI Creation The tab Multiple ROI Creation allows for the automatic creation of ROIs useful for the rapid classifica tion of multi temporal images or for accuracy assessment see this tutorial It performs the region growing of ROIs at user defined points requiring a list of point coordinates and class definitions Created ROIs are automati cally saved to the Training shapefile page 89 The following video shows this tool http www youtube com watch v MxBwMOQnyZKw Point coordinates and ROI definition Table fields X float point X coordinate Y float point Y coordinate MC ID P ROI Macroclass ID int MC Info P ROI Macroclass information text C ID P ROI Class ID int 102 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Semi Automatic Classification Plugin gow Fig 17 1 Multiple ROI Creation 17 1 Tools 103 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 C Info P ROI Class information text Min size int the minimum area of a ROI in pixel unit corresponding to Min ROI size in
45. remove the final 0 if present e g rename B10 to B1 25 3 Can apply the Landsat conversion and DOS correction to clipped bands Yes you can clip the images before the conversion to reflectance and then copy the MTL file contained in the Landsat dataset inside the directory with the clipped bands If you want to apply the DOS correction which is an image based technique you should convert the original Landsat bands the entire image and then clip the conversion output i e bands converted to reflectance 25 4 Can I apply the DOS correction to Landsat bands with black border i e with NoData value If you want to apply the DOS correction to an entire Landsat band which has NoData values the black border with value 0 then you have to check the checkbox Use NoData value and set the value to 0 This is because DOS is an image based technique and NoData values must be excluded from the calculation 203 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 25 5 How to remove cloud cover from Landsat images DOS correction does not remove clouds from the image However Landsat 8 images include Band 9 that identi fies clouds see this NASA site You can use this band for the creation of a mask For other Landsat satellites clouds can be masked using the approach described this paper Also see the following video tutorial 25 6 How do create a virtual raster manually in QGIS In order to create a
46. right region of the image In order to create manually a ROI inside the dark area click the button b in the ROI creation page 92 Left click on the map to define the ROI vertices and right click to define the last vertex closing the polygon An orange semi transparent polygon is displayed over the image which is a temporary polygon i e it is not a shapefile 3 fo ga YES E ut M m e e ne LIA color MciD MC Info ci C Info 2 A e ie n e y a lla s 8 TI mer menn Caseros i L B R CEEE 8 M AV select chassification algorithm Threshold ee li qt Minimum Distance w o 0000 b ao asa Fig 10 4 A temporary ROI created manually It is required to define the Classes and Macroclasses page 39 In the ROI Signature definition page 93 set MC ID 21andMC Info Water also setC ID 1andC Info Lake In order to save the polygon in the Training shapefile click the button Save ROI After a few seconds the ROI is listed in the ROI list page 91 Also the spectral signature is calculated and listed in Signature list page 95 because Add sig list was checked in Classes and Macroclasses page 39 Now we have created the first ROI Zoom in the map over the blue area it is built up in the upper left region of the image In order to create a ROI with the automatic region growing algorithm in ROI parameters page 91 set the Range radius value to 2000 this value depends on image range of pixel v
47. smoleneports Upgrade Install pugn one 9 Author Luca Congedo The SCP should be automatically activated however be sure that the Semi Automatic Classification Plu gin is checked in the menu Installed the restart of QGIS could be necessary to complete the SCP installation um Plugin installed successfully o E Y installed Search Notinstabed AE Remove empty layers from the ml 5 SE p ees Semi Automatic Classification Plugin o opc Plugin for the semi automatic supervised classification designed to expedite the Sen Rss menu processing of multispectral or hyperspectral remote sensing images which provides a RT MapServer Exporter set of tools for pre processing and post processing RT Omero 2 Written by Luca Congedo the Semi Automatic Classification Plugin SCP allows for the semi automatic RT QSpider supervised classification of remote sensing images providing tools to expedite the creation of ROIs training RuGeocoder areas through region growing or multiple ROI creation The spectral signatures of training areas can be 5 R automatically calculated and displayed in a spectral signature plot It is possible to import spectral signatures from external sources Also a tool allows for the selection and download of spectral signatures di srw from the USGS Spectral Library http spedab cr usgs gov spectral lib html Several tools are available for E scosumer the pre processing phase
48. spatial reference system Missing or invalid CRS Output spatial reference system Use predefined spatial reference system EPSG 4326 WGS 84 Choose __ Import spatial reference system from existing layer Fig 20 34 Define the shapefile projection Now we can clip the classification mosaic tif Load the classification in QGIS Open the command Raster gt Extraction gt Clipper Select the classification mosaic as input raster set the out put file e g classification clip tif and set No data value equals to 0 In Clipping mode enable Mask layer and select costa rica then click OK Finally we have a classification clipped to the extent of Costa Rica as you can see we would need other classifi cations for covering the whole extent of Costa Rica and we can calculate the classification report 20 9 Classification Report In SCP open the tab Classification report page 121 and click the buttons Refresh list Check Use NoData value setting the value equals to O and click the button Calculate classification report The classification report is displayed with the count of pixels the area and percentage of each land cover class You can save the report to text file clicking the button Save report to file We have completed this tutorial about the land cover classification of a large area using multiple Landsat images and creating a classification mosaic It is worth pointing out that classification results depend
49. the ESA European Space Agency website https scihub esa int dhus Sentinel 2 is a new European satellite developed in the frame of Copernicus land monitoring services which acquires 13 spectral bands with the spatial resolution of 10m 20m and 60m depending on the band see Sentinel 2 Satellite page 34 A free registration is required in order to access to ESA data see https scihub esa int userguide 1SelfRegistration The search is performed using the Data Hub API The following video shows this tool http www youtube com watch v fVS2Ls2bUbk Alternative video link https archive org details video_tutorial_download_sentinel_SCP Login Sentinels https scihub esa int dhus In order to access to Sentinel data a free registration is required at https scihub esa int userguide ISelfRegistration After the registration enter the user name and password for searching and accessing data 112 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 User enter the user name Password enter the password remember remember user name and password in QGIS Area coordinates Define the search area click the map for the definition of the Upper Left UL and Lower Right LR point coordinates X and Y of the rectangle defining the search area it is possible to enter the coordinates manually Search Define search settings such as the date of acquisition or
50. the category Unclassified which means that cloud pixels are not classified i e masked seee Edt View Layer Settings Plugins Vector Raster Database Web SCP Processing Help S Bm PP lum SITTERS WEE RUE el eS Bs Lai XID th Be ll de t e SCP ROI creation SCP Classification V mun x ee o e Reset v d EA New shp Ve E D e S ICIv MCinfoi CID Cinfo Color MC ID E HEU EET E Dv C Info e A l vio Uncla 24 Clouds Bi 11 Built up 1 Built up1 I 2 v 1 Bultup Built 2 2 Vegetation 2 Tees e 372 Veget 2 Trees 30 Unclassified 24 Clouds a E 4 v3 Soil 26 Soil7 43 Soil 26 Soil7 S w 4 Water 31 Water7 S4 water 31 Water7 a D See 5 la lo Le lS L port import Add to signature mm FT z 95 CEEE cy 4d Vv y Select classification algorithm Threshold Range radius Min ROI size Max ROI width dt Maximum Likelihood v 0 0000 gt al 0 006000 60 gt 100 lt A 7 Use Macroclass ID v Rapid RO on band 36 2 as _ Automatic refresh RO Automatic plot a d edos J e show D i Y Display cursor for NDVI v E ES f 3 MD Mino __ Apply mask Reset 0 IT Unclassified i u Create vector __ Classification report Cib Cinfo Perform classification 24 I Clouds E SCP Classification Layers _Save ROI v Add sig list Undo Coordinate 835194 1107752 Scale 1 57 290 v Rotation 0 0 y Render EPSG 32616 Fig 20 16 ROI created for cloud masking In the foll
51. the virtual raster and hide all the single band rasters from the QGIS Layers Project Edit View Layer Settings Plugins Vector Raster Database SCP Help lE El ER 2 DEN lt lt band set gt gt vs RES Y um bX robb Do th ge Be ea MA Layers ex SCP ROI creation 9 m O v DF band set vrt M x vv ROI ROI voy New shp pS CI g FF dip RT Lc81910312015006LGNO00 P Z IF clip RT LC81910312015006L GNOO MCID MCinfo CID Cinfo a t o Bf clip RT LC81910312015006LGN00 B i a e Jb gt BF dip RT Lce1910312015006t Guo0 B2 utup A gp 3 3 Vegetation 3 Vegetation IF clip_RT_1C81910312015006LGNO00 ee 4 4 Bare soil 4 Bare soil Y clip RT Lc81910312015006L GNOO e e DD Y Add to signature pa LA E A ROI p a Vee Min ROI size Max ROI width M 0 010000 gt 60 100 x 2 Rapid ROI on band 19 2 _ Automatic refresh ROI Automatic plot a RO Signature definition 3 MC 1D MC info 1 Ji Macroclass 1 ciD C Info 1 Class 1 the e acr E f ia SCP Classification Layers Save RO w Add sig list 305274 4621653 Fig 11 13 Color composite RGB 3 2 1 In the Toolbar page 87 type 3 4 6 in the list RGB Using this color composite urban areas are purple and vegetation is green You can notice that this color composite RGB 3 4 6 highlights roads more than RGB 3 2 1 See Create the ROIs page 58 for the details about the ROI creati
52. tif The process produces an error matrixandanerror raster which are useful for assessing the quality of our classification 20 6 Mosaic of Classifications 173 lassificatio Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Project Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help DARRA F Zum IVA s WO ih fle i ie e qy Layers VER SCP ROI creation o x amp Ve Kex E aR VEG F Semi Automatic Classification Plugin Dv wv accuracy tif y son EP Tools ell Preprocessing Be Postprocessing Bandcale mm Bandset X Settings P At E 1 Da E Accuracy E Landcoverchange Classification report lt 2 Classification to vector 23 Reclass A 25 0 Error Matrix input ee Y E Nm Select the classification to assess classification mosaic v PP dassification mosaic em Select the reference shapefile or raster ROL v Shape le field MCID Uc e Calculate error matrix i 4 0 o 1 Vg 4 Water so o s9106 Y PP dassification 3 douds 25 0 Unclassified V e 4 Water v I dassification 2 douds pad lassification 1 clouds Unclassified s 1 Built up 2 Vegetation 3 Soil 5 4 Water M Show docks ZZ Quick user guide 7 Online help SCP Classification Layers Yo muy ary ras 1 legend entries removed 5 Coordinate Scale 1 318 820 v Rotation 0 0 y Render EPSG 32616 Fig 20 32 Accuracy assessme
53. two bands Pixel values for two raster bands are represented as points in the 2D space Fig 19 1 Scatter Plot 141 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 19 1 ROI List Table fields S checkbox field MC ID Macroclass ID MC Info Macroclass Information C ID Class ID C Info Class Information Color color field double click to select a color for the plot e lt Band X X band of the plot Band Y Y band of the plot Calculate scatter plot calculate the scatter plot for the ROI checked in the list it can be time consuming Plot commands from Matplotlib i Reset to original view Back to previous view Forward to next view Pan axes with left mouse zoom with right Zoom to rectangle Unused Save plot to a figure e g JPG file soen o Unused 142 Chapter 19 Scatter Plot Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Fig 19 2 Example Scatter Plot 19 1 ROI List 143 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 144 Chapter 19 Scatter Plot Part V Thematic Tutorials 145 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 The following are thematic tutorials Before these tutorials it is recommended to read the Basic Tutorials page 55 Tutorial Land Cover Classification and Mosa
54. two docks and a toolbar should be added to QGIS Also a SCP menu is available in the Menu Bar of QGIS It is possible to move the Toolbar page 87 and the docks according to your needs as in the following image 16 Chapter 3 Installation in Ubuntu Linux Semi Automatic Classification Plugin Documentation Release 4 8 0 1 d la s b Le kg L L Eeport import 2 left Minimum Distance y 0 0000 di Size 200 The configuration of available RAM is recommended in order to reduce the processing time From the SCP menu page 85 select QA Settings gt Processing In the Settings page 129 set the Available RAM MB to a value that should be half of the system RAM For instance if your system has 2GB of RAM set the value to 1024MB 3 3 Configuration of the plugin 17 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 18 Chapter 3 Installation in Ubuntu Linux CHAPTER 4 Installation in Debian Linux 4 1 QGIS download and installation Open a terminal and type sudo apt get update Press Enter and type the user password Type in a terminal sudo apt get install qgis python matplotlib python scipy Press Enter and wait until the software is downloaded and installed Now QGIS 2 is installed z Project Edit View Layer Settings Plugins Vector Raster Database Processing Help a E TEE D qm 79 A AA G E S A a ty
55. usually expressed as RGB Br Bg Bb where R stands for Red G stands for Green B stands for Blue Bris the band number associated to the Red color e Bg is the band number associated to the Green color Bbis the band number associated to the Blue color The following Figure Color composite of a Landsat 8 image page 36 shows a color composite R G B 24 32 of a Landsat 8 image for Landsat 7 the same color composite is R G B 32 1 and a color composite R G B 5 4 3 for Landsat 7 the same color composite is R G B 4 3 2 The composite R G B 5 4 3 is useful for the interpretation of the image because vegetation pixels appear red healthy vegetation reflects a large part of the incident light in the near infrared wavelength resulting in higher reflectance values for band 5 thus higher values for the associated color red 6 9 Pan sharpening Pan sharpening is the combination of the spectral information of multispectral bands MS which have lower spatial resolution for Landsat bands spatial resolution is 30m with the spatial resolution of a panchromatic band PAN which for Landsat 7 and 8 it is 15m The result is a multispectral image with the spatial resolution of the panchromatic band e g 15m In SCP a Brovey Transform is applied where the pan sharpened values of each multispectral band are calculated as Johnson Tateishi and Hoan 2012 M Span MS PAN I where J is Intensity which is
56. 0 Ls Rapid Won band Automatic refresh ROI _ Automatic pot v Display cursor for NOVI y 0 Show ROL Lr E ko ktm a a CJ Bare soll i SC Clssiicaion Layers Save ROL y addsig st Suse Jste 113205 T Fig 11 15 Creation of a ROI displaying OpenStreetMap Project Edt View Layer Settings Plugins Vector Raster Database SCP Help O BBA EE vo dip RT LC81910312015006LGN00 lip RT LCR19103120150061 GNO0 BF clip Rr Lctt 910312015004 M00 IF dip rr LCOL91O312015006L GOO DSSSBSS A 485 M automatic rehesh ROI _ Automatic pot SC Le ieee ETECIES Y Y Display cursor or Novi vi s Show ROL r 7 sol Save ROL_ v addsig ist Disalin LR A ner ess 2e57 0 Fig 11 16 The same ROI displaying the color composite RGB 3 2 1 11 6 Create the ROIs 75 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 rF Fe 1 War Fig 11 18 Built up ROI large buildings 76 Chapter 11 Tutorial 2 Land Cover Classification of Landsat Images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 RGB 3 2 1 RGB 4 3 2 RGB 3 4 6 Fig 11 19 Built up ROI road RGB 3 2 1 RGB 4 3 2 RGB 3 4 6 Fig 11 20 Built up ROI buildings and narrow roads RGB 3 2 1 RGB 4 3 2 RGB 3 4 6 Fig 11 21 Bare soil ROI uncultivated land RGB 3 2 1 RGB 4 3 2 RGB 3 4 6 Fig 11 22
57. 0 5 3 classification which means that if NDVI value is greater than 0 5 assign the pixel value 3 i e Vegetation otherwise leave the original classification value Click the button Calculate select where to save the new classification e g a new file named refined_classification The new classification is added to QGIS It is possible to copy the style from the original classification in QGIS Layers right click on the layer name and select Copy style and paste it to the new classification right click on the layer name and select Paste style 21 3 Classification refinement basing on NDVI values 183 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 F o Semi Automatic Classification Plugin E Tools 4 Pre processing Bb Postprocessing Band calc sz Band set Settings 7 About raster classification raster2 NDVI tif raster3 masked 6 tif ractar Expression S tif np where NDVI tif gt 0 5 3 classification re RENN 0751756 0 0870922 RI LCSI910312015006LGNO0 07 0 010000 6 Fig 21 10 The output land cover classification with color style 184 Chapter 21 Tutorial Using the tool Band calc Semi Automatic Classification Plugin Documentation Release 4 8 0 1 You can see that now a larger area is classified as vegetation 21 3 Classification refinement basing on NDVI values 185 Semi
58. 057LGN00 calculate NDVI with the following expression RT LC80160532014057LGN00 B5 RT_LC80160532014057LGNO0_B4 RT LC801605320 14057LGNOO_B5 and the following expression for enhancing the classification np where NDVI_2 gt 0 5 2 classification_2 Project Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help S BET oz et A AMR BRA ADO ee se e Layers MOS x SCP ROI creation ox 9 ado Y classification 2 NDVI vj s Newshp V i vam N D 1 Built up MC ID MC Info cio C info 2 Vegetation a 3 Soil 4 Water v F classification 1 NDVI 0 Unclassified 1 Built up 2 Vegetation 3 Soil 4 Water t S g e 3 10 0 YMaSDaAINARS Y BP novi 1 tif Add to signature STATES A n RES RO parameters Vey i BF classification 1 Range radius Min ROI size Max ROI width 0 Unclassified 0 010000 60 100 1 Built up fand s 2 Vegetation 8 3 Soil Automatic refresh ROI Automatic plot 4 Water gt BF RT_LC80150532014050LGNOO E 0 i e Show 0 376693 e BO 2 WW RT_LC801505320140501 GNOO Y Display cursor for NDVI v E gt Mo os had 0 529091 RO Signature definition E na gt I RT 1C80150532014050L GNOO MCID MC info E a mo Tm 1 Macroclass 1 v BP RT_LC80150532014050LGNOO ci Cinfo ron an v 1 SM Class 1 E SCP Classification Layers Y
59. 1 Bul l Buk 32 Vegetation 3 Grassland 2 4 4 v2 Veg 8 Shr 4 3 Soil 4 Soill r1 5 42 Veg l4 Shr 813 sol S Soll2 et e 6 v2 Veg 13 Shr 6 2 Vegetation 6 Forest a Y IN Men 12 Cro v 7 0 Unclassified 7 Clouds v y Lalo tolle Ji s Export import ts 9 SEE a e y 2 Select classification algorithm Threshold Range radius Min ROI size Max ROI width eg de Maximum Likelihood w 0 0000 A 0 006000 60 100 v Use Macroclass ID v Rapid ROI on band 30 ge E Automatic refresh ROI Automatic plot a Si 500 9 feos P o Show Transparency Redo Y a Os o 5 SRC Y Display cursor for NOM_v a L Select qmi Reset r uM Mc 10 MC info e Ig a 0 T Unclassified in __ Create vector _ Classification report eo Cinfo e Perform classification 24 C M Clouds SCP Classification Layers _Save ROI_ V Add sig st Undo _ 1 legend entries removed Coordinate 9232883 1256040 Scale 1 1 491 471 v Rotation 0 0 C y Render EPSG 3857 0T Fig 20 19 Land cover classification 1 of the Landsat image LC80150532014050LGN00 We can see that part of the clouds are black i e unclassified however several cloud pixels are classified as Built up Also the black border of the Landsat image is classified as Built up We are going to correct these errors and refine the classification in the next steps Now in QGIS open the following Landsat 8 bands that are inside the directory LC80160532014057LGN00 converted e R
60. 2015006LGN00_B2 np where LC81910312015006LGN00_BQA 53248 0 RT_LC81910312015006LGN00_B3 np where LC81910312015006LGN00_BQA 53248 0 RT LC81910312015006LGN00_B4 np where LC81910312015006LGN00_BQA 53248 0 RT LC81910312015006LGN00 B5 6 7 np where LC81910312015006LGNO00 BQA 53248 0 RT LC81910312015006LGNO00 B6 np where LC81910312015006LGN00_BQA 53248 0 RT LC81910312015006LGNO00 B7 Fig 21 3 The expression in Band calc TIP If the text in Expression is green it means that the syntax is correct otherwise it is red and the button Calculate is disabled Click the button Calculate select where to save the bands e g a new directory named masked bands and write the output name e g masked Multiple outputs are created with the same output name and a numerical suffix based on the numerical order of the expressions Calculated bands are also added to QGIS According to the order of expressions the file masked 1 corresponds to the band RT LC81910312015006LGNO00 B2 the file masked 2 corresponds to the band RT LC81910312015006LGNO00 B3 and so on Masked pixels have NoData values i e nan 21 2 NDVI Calculation NDVI is an index calculated as Near Infrared band Red band Near Infrared band Red band which ranges from 1 to 1 Green vegetation has the highest NDVI values tending to 21 2 NDVI Calculation 181 Semi Automatic Classification Plugin Documentation
61. 4 8 0 1 Semi Automatic Classification Plugin Plugin for the semi automatic supervised classification designed to expedite the processing of multispectral or hyperspectral remote sensing images which provides a set of tools for pre processing and post processing Written by Luca Congedo the Semi Automatic Classification Plugin SCP allows for the semi automatic supervised classification of remote sensing images providing tools to expedite the creation of ROIs training areas through region growing or multiple ROI creation The spectral signatures of training areas can be automatically calculated and displayed in a spectral signature plot It is possible to import spectral signatures from external sources Also a tool allows for the selection and download of spectral signatures from the USGS Spectral Library http spedab cr usgs gov spectral lib html Several tools are available for the pre processing phase image clipping Landsat conversion to refiectance the classification process Minimum Distance Maximum Likelihood Spectral Angle Mapping algorithms and classification previews and the post processing phase conversion to vector accuracy assessment land cover change classification report This plugin requires the installation of GDAL OGR Numpy SciPy and Matpl machine is available http fromgistors blogspot com p semi automatic os html Keywords POF BR HEREDA PTEN ao Ml alaia ias Class
62. 7 Science Data User s Handbook Available at http landsathandbook gsfc nasa gov Richards J A and Jia X 2006 Remote Sensing Digital Image Analysis An Introduction Berlin Germany Springer 7 8 Accuracy Assessment 45 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 46 Chapter 7 Supervised Classification Definitions CHAPTER 8 Landsat image conversion to reflectance and DOS1 atmospheric correction This chapter provides information about the Landsat conversion to reflectance implemented in SCP Landsat page 114 Landsat images downloaded from http earthexplorer usgs gov or through the SCP tool Download Landsat page 108 are composed of several bands and a metadata file MTL which contains useful information about image data 8 1 Radiance at the Sensor s Aperture Radiance is the flux of energy primarily irradiant or incident energy per solid angle leaving a unit surface area in a given direction Radiance is what is measured at the sensor and is somewhat dependent on reflectance NASA 2011 p 47 The Spectral Radiance at the sensor s aperture 4 is measured in watts meter squared ster um and for Landsat images it is given by https landsat usgs gov Landsat8_Using_Product php Ly Mr Qeat Ar where e Mz Band specific multiplicative rescaling factor from Landsat metadata RADI ANCE MULT BAND x where x is the band number Ar Band speci
63. AI GNAN RA Expression Fig 21 2 The Band calc tool 180 Chapter 21 Tutorial Using the tool Band calc Semi Automatic Classification Plugin Documentation Release 4 8 0 1 np where condition value if true value if false Where e condition is a logical condition between bands or values e value if trueandvalue if false can bea numerical value a band or another expression In Expression enter the following block of expressions np where LC81910312015006LGN00 BQA 53248 0 RT LC81910312015006LGN00 B2 np where LC81910312015006LGN00 BQA 53248 0 RT LC81910312015006LGN00 B3 np where LC81910312015006LGN00 BQA 53248 0 RT LC81910312015006LGN00 B4 np where LC81910312015006LGN00 BQA 53248 0 RT LC81910312015006LGN00 B5 np where LC81910312015006LGN00 BQA 53248 0 RT LC81910312015006LGN00 B6 np where LC81910312015006LGN00 BQA 53248 0 RT LC81910312015006LGN00 B7 E w Semi Automatic Classification Plugin Yow EF Tools f Preprocessing JP Post processing Band calc gag Band set Settings 7 About Raster bands Variable E Band name RT LC81910312015006LGN00 B7 i 1 2 raster2 RT_LC81910312015006LGN00_B6 Refresh list al bs i j raster raster3 RT_LC81910312015006LGN00_B5 RT 1F2101N219N1SANAILGNAN RA Y Expression O np where LC81910312015006LGN00_BQA 53248 0 RT_LC8191031
64. Add sig list R Coordinate 767881 998699 Scale 1 451 348 v Rotation 0 0 C v Render EPSG 32616 4 Fig 20 26 Classification 2 refined with NDVI For the Landsat 7 image LE70150532014090EDC0O0 calculate NDVI with the following expression RT LE70150532014090EDCOO B4 RT LE70150532014090EDCOO B3 RT LE701505320 20 4 Enhancement of Classification Using NDVI 169 LA4090EDCOO B4 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 and the following expression for enhancing the classification np where NDVI 3 gt 0 5 2 classification 3 Project Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help ME AA pu Je bBo zm E vie Show fa f b EP ER ih fle Mae V Layers ox SCP ROI creation gt x Na 9 A le s Mer z Y gt dassification 3 NDVI pies abo E a Unclassified P 2 1 Built up MCID MC info co C info E A 2 Vegetation IS o Soil Water a F dassification 2 NDVI 0 Unclassified qe Hu 2 Vegetation 8 3 Soll 5 S5 4 Water K v F dassification 1 NDVI Add to signature be Lia 9 0 Unclassified o 1 Built up MEET AE AULAS 2d V 2 Vegetation Range radius Min ROI sze Max ROI width r G 3 Soil 0 010000 60 100 4 Water x Re F nov 1 tif _ Rapid RO on band 1 0 222 _ Automatic refresh ROI Automatic plot a 0 856 s W dassific
65. Automatic Classification Plugin Documentation Release 4 8 0 1 186 Chapter 21 Tutorial Using the tool Band calc CHAPTER 22 Other Tutorials Visit the blog From GIS to Remote Sensing for other tutorials such as Supervised Classification of Hyperspectral Data Monitoring Deforestation Flood Monitoring Estimation of Land Surface Temperature with Landsat Thermal Infrared Band Land Cover Classification of Cropland For other unofficial tutorials also in languages other than English see Where can I find more tutorials about SCP also in languages other than English page 211 187 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 188 Chapter 22 Other Tutorials Part VI Semi Automatic OS 189 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 The Semi Automatic OS is a lightweight virtual machine for the land cover classification of remote sensing im ages It includes the Semi Automatic Classification Plugin for QGIS already configured along with all the re quired dependencies Fig 22 1 Semi Automatic OS desktop The Semi Automatic OS is based on Debian and it is designed to require very little hardware resources It uses LXDE and Openbox as main desktop environment This virtual machine can be useful for testing the Semi Automatic Classification Plugin or when the installation of the required programs in the host system is problem atic
66. Cc ETE aus Y 7 COE Fig 11 5 Download Landsat 8 bands 68 Chapter 11 Tutorial 2 Land Cover Classification of Landsat Images Download Landsat Semi Automatic Classification Plugin Documentation Release 4 8 0 1 page 115 Also the metadata information for each band is loaded because the metadata file MTL txt is inside the same directory TIP If the metadata file MTL txt was in a different directory one can click the button Select MTL file and select the file Also it is possible to edit the metadata information inside the table Metadata page 115 In order to calculate surface reflectance we are going to apply the DOS Correction page 48 therefore enable the option Apply DOS1 atmospheric correction TIP It is recommended to perform the DOS1 atmospheric correction to the entire Landsat image before clipping the image in order to improve the calculation of parameters based on the image Uncheck the option Create Band set already enabled In order to start the conversion process click the button Perform conversion and select the directory where converted bands are saved e g Landsat RT P Semi Automatic Classification Plugin YY ES E Tools E Pre processing 2 Post processing E Bandcalc sm Bandset A Settings Ep About YY Landsat af Clip multiple rasters Split raster bands Landsat conversion to TOA reflectance and brightness temperature Directory containing L
67. Correction 49 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Chavez P S 1996 Image Based Atmospheric Corrections Revisited and Improved Photogrammetric Engineering and Remote Sensing Falls Church Va American Society of Photogrammetry 62 1025 1036 Finn M P Reed M D and Yamamoto K H 2012 A Straight Forward Guide for Process ing Radiance and Reflectance for EO 1 ALI Landsat 5 TM Landsat 7 ETM and ASTER Unpublished Report from USGS Center of Excellence for Geospatial Information Science 8 p http cegis usgs gov soil_moisture pdf A 20Straight 20Forward 20guide 20for 20Processing 20Radiance 20and 2 Moran M Jackson R Slater P amp Teillet P 1992 Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output Remote Sensing of Environment 41 169 184 NASA Ed 2011 Landsat 7 Science Data Users Handbook Landsat Project Science Office at NASA s Goddard Space Flight Center in Greenbelt 186 http landsathandbook gsfc nasa gov pdfs Landsat7_Handbook pdf Sobrino J Jim nez Mu oz J C amp Paolini L 2004 Land surface temperature retrieval from LANDSAT TM 5 Remote Sensing of Environment Elsevier 90 434 440 50 Chapter 8 Landsat image conversion to reflectance and DOS1 atmospheric correction CHAPTER 9 Conversion to At Satellite Brightness Temperature This chapter provides information about the Landsat conve
68. Data Users Handbook by NASA Remote Sensing Note by JARS 28 2 Where can ask a new question A Facebook group and a Google Community are available for sharing information and asking for help 28 3 Where can find more tutorials about SCP also in languages other than English There are several tutorials about SCP on the internet Following an incomplete list of these resources French Suivre l impact des feux de for ts par imagerie satellite avec le plugin Qgis SCP German 2015 Jakob Erfassung von Landnutzungsver nderungen mit FOSS Image Processing Tools Italian Classificazione e Mosaico di Varie Immagini Landsat Korean QGIS Semi Automatic Classification Plugin Portuguese Classifica o Supervisionada de Imagens Orbitais com o Semi Automatic Classification Plu gin Portuguese Tutorial Classifica o e caracteriza o de imagens de sat lites Portuguese Aprendizagem Supervisionada usando o SCP no QGIS Portuguese Classificag o supervisionada utilizando o QGIS e SCP Spanish Ejercicio Clasificaci n Semiautom tica Plugin SCP Spanish Aplicaciones de Teledetecci n con el QGIS y el plugin Semi Automatic Classification Spanish Descarga de Landsat 8 7 5 y 4 Semi Automatic Classification Plugin Qgis 2 8 Swedish Landsat 8 och fj rranalys med QGIS Ukrainian 211 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 28 4 How can I translate this user manual to another language
69. Installation Semi Automatic Classification Plugin Documentation Release 4 8 0 1 The Semi Automatic Classification Plugin requires the installation of GDAL OGR NumPy SciPy and Mat plotlib This chapter describes the installation of the Semi Automatic Classification Plugin for the supported Operating Systems Semi Automatic Classification Plugin Documentation Release 4 8 0 1 CHAPTER 1 Installation in Windows 32 bit 1 1 QGIS download and installation Download the latest QGIS version 32 bit from here the direct download of QGIS 2 8 from this link Execute the QGIS installer with administrative rights accepting the default configuration Now QGIS 2 is installed ATA Project Ede wew Layer Settings Plugns Vector Raster Database Web Processing Heb DOBAI NERVIO WB MOS PEIPLADO A BRN i e D ie eG e Hom tas K 9 th Qr rz iy Lo ES Em y es i zs es JA ss se ie n es 1 2 Semi Automatic Classification Plugin installation Run QGIS 2 From the main menu select Plugins Manage and Install Plugins Project Edit View Layer Settings Plugns Vector Raster Database Web Processing Help Manage and Install Plugins 068 BQ Acne 2G fZzG2SE 2V H suu From the menu A11 select the Semi Automatic Classification Plugin and click the button Install plugin Semi Automatic Classification Plugin Documentation Release
70. Landsat Images e io moce oo oo go m nomo kom m Ro o9 OX ES 20 4 Enhancement of Classification Using NDVI o oo e e 20 3 Cloud Maskin o ox Roy RR EUR oU Cox Reo oem Xe ec OR al eae 20 6 Mosaic of Classifications 0 ue em ges ERR E RR RR Re E UR CR RD RR RC ER COR 20 7 Accuracy Assesst enL os ea obo Ron A won wo o e EUR AUR S XR d Dog 20 8 Chipot the Classification s s i doxes onem e etd d S bad dou we de dd S es 20 9 Classification REPOT z 6 lt lt svete 34 GAS OU a A EUN E CES EUR 21 Tutorial Using the tool Band calc 21 1 Application ofa mask to multiple bands ee ee ee ee 21 2 NDVI Calculation i s motum mu bing hm Seek Sido RR GR Re REDE ed d b 21 3 Classification refinement basing on NDVI values o 22 Other Tutorials VI Semi Automatic OS 23 Installation in VirtualBox VII Frequently Asked Questions 24 Plugin installation 24 1 How to install the plugin manually ss soeu eaea se RR RR 25 Pre processing 25 1 Which image bands should I use for a semi automatic classification 25 2 Which Landsat bands can be converted to reflectance by the SCP lens 25 3 Can I apply the Landsat conversion and DOS correction to clipped bands 25 4 Can I apply the DOS correction to Landsat bands with black border i e with NoData value 25 5 How to remove cloud cover from Landsat images oo e 25 6 How do I c
71. ROI parameters page 91 Max width int the maximum width of a ROI corresponding to Max ROI width in ROI parameters page 91 Range radius float the interval which defines the maximum spectral distance be tween the seed pixel and the surrounding pixels in radiometry unit corresponding to Range radius in ROI parameters page 91 Rapid ROI band int if defined ROI is created only on the selected band correspond ing to Rapid ROI on band in ROI parameters page 91 Add point add a new row to the table for the definition a point all the table fields must be filled for the ROI creation e Create random points create random points inside the input image area the point amount is defined in Number of random points e Number of random points seta number of points that will be created when Create random points is clicked inside a grid of cell size if checked the input image area is divided in cells where the size thereof is defined in the combobox image unit usually meters points defined in Number of random points are created randomly within each cell minimum point distance if checked random points have a minimum distance defined in the com bobox image unit usually meters setting a minimum distance can result in fewer points than the number defined in Number of random points e Remove highlighted points delete the highlighted rows from the table e Import point list import
72. Release 4 8 0 1 Fig 21 4 Masked bands 1 Open the Band calc page 125 and click the button Refresh list Clear the content of Expression and write the following expression for the calculation of NDVI masked_4 tif masked 3 tif masked_4 tif masked 3 tif where masked 4 tif is the Near Infrared band and masked 3 tif is the Red band p amp Semi Automatic Classification Plugin Yow EP Tools af Pre processing Jp Postprocessing Band calc ge Band set 9 Settings 7 About SS Rasera s o i raster masked_6 tif raster2 masked_5 tif i Refresh list raster3 masked 4 tif Bm atif Expression masked 4 tif masked 3 tif masked_4 tif masked 3 tif Fig 21 5 The expression in Band calc TIP The expression can work both with Variable and Band name between quotes Also bands in the Band set page 127 can be referenced directly for example bandset b1 refers to band 1 of the Band set Double click on any item in the Band list page 126 for adding its name to the expression Click the button Calculate select where to save the NDVI e g a new file named NDVI The NDVI is added to QGIS 182 Chapter 21 Tutorial Using the tool Band calc Semi Automatic Classification Plugin Documentation Release 4 8 0 1 sch processing Heb ahb F E SACO Liehiess e6 B SSCP ROI creation Ps Project Va Prenrostacee am J a Sew amp
73. Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Luca Congedo October 29 2015 Contents Plugin Installation 3 Installation in Windows 32 bit 7 1 1 QGIS download and installation ee 7 1 2 Semi Automatic Classification Plugin installation o e 7 13 Configurationiol the plUgll s sors e s RR a A E Ee a EN ae a 8 LA KnOWIDISSUIES e oiga ad as aa 9 Installation in Windows 64 bit 11 2 1 QGIS download and installation gt lt lt lt s 222 sp REG HHS 11 2 2 Semi Automatic Classification Plugin installation o o 11 2 3 Configuration of theplugin y ass oon ee a e A CES ewe SSeS BAG 12 Installation in Ubuntu Linux 15 3 1 QGIS download and installation ee 15 32 Semi Automatic Classification Plugin installation o 15 3 3 Configuration of the plugin 2 2 04 555 4545 4 54 2 p n ROG a a 16 Installation in Debian Linux 19 4 1 QGIS download and installation es 19 4 2 Semi Automatic Classification Plugin instalation o less 19 4 3 Conhpurationof the plus ou aa a a A ERE EIS EA 20 Installation in Mac OS 23 5 1 QGIS download and installation 2 2 e ee 23 5 2 Semi Automatic Classification Plugin instalation lee 23 2 9 Configuration of the plugin rarse A RR A OR 24 Brief Introduction to Remote Sensing 27 Basic Definitions 31 0 1 GIS definition s gone eka aee e ea A Eo oe EOS
74. T LC80160532014057LGNO00 B2 tif Blue e RT LC80160532014057LGNO00 B3 tif Green e RT LC80160532014057LGNO00 B4 tif Red e RT LC80160532014057LGNO0 B5 tif Near Infrared e RT LC80160532014057LGNO00 B6 tif Short Wavelength Infrared 1 RT LC80160532014057LGNO0 B7 tif Short Wavelength Infrared 2 Repeat the above steps for the creation of the Band set the Training shapefile and Signature list file TIP close QGIS and create a new QGIS project for each Landsat image in order to delete temporary files and free disk space Create a land cover classification repeating the steps previously described In a new QGIS project open the Landsat 7 bands inside the directory LE70150532014090EDCO00 converted e RT LE70150532014090EDCO00 BI tif Blue e RT LE70150532014090EDCO0 B2 tif Green RT LE70150532014090EDCO0 B3 tif Red e RT LE70150532014090EDCO00 B4A tif Near Infrared RT LE70150532014090EDCO0 B5 tif Short Wavelength Infrared 1 RT LE70150532014090EDCOO0 B7 tif Short Wavelength Infrared 2 20 3 Classification of Landsat Images 165 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 ces Edt View Layer Settings Plugins Vector Raster Database Web SCP Processing Help SBR l F a lt lt bandset gt gt vs ES 432 ve Show A X bS EF ME El aO e te ee gt VS so Classification x SCP ROI creation ex 9 Y open Save Reset RO E y JLo Newshp g T 9 TS Civ MCinfo CIO
75. Use shapefile for clipping if checked use the selected shapefile already loaded in QGIS for clipping overriding point coordinates e No data value gt set the value for NoData pixels e g pixel outside the clipped area Output name prefix gt set the prefix for output file names e Clip selected rasters choose the output destination and clip selected rasters only rasters selected in the Raster list page 117 are clipped and automatically loaded in QGIS 17 2 3 Split raster bands Split a multiband raster to single bands Select a multiband raster select a multiband raster already loaded in QGIS Output name prefix gt set the prefix for output file names e Split selected rasters choose the output destination and split selected raster output bands are automatically loaded in QGIS Raster input 17 3 Post processing The tab Post processing provides several functions that can be applied to the classification output 17 3 1 Accuracy The tab Accuracy allows for the validation of a classification read Accuracy Assessment page 44 Classi fication is compared to a reference raster or reference shapefile which is automatically converted to raster If a shapefile is selected as reference it is possible to choose a field describing class values Several statistics are calculated such as overall accuracy user s accuracy producer s accuracy and Kappa hat The output is an error raster that i
76. _ Rapid RO on band 1 Automatic refresh ROI Automatic plot a mado 38 Je Show O n Y Display cursor for NDVI v Al Select Reset le 3 PR M mask 2 Reset E e Ain fas 1 Macroclass 1 U __ Create vector __ Classification report Ko Kw e Perform classification 1 I Class 1 Ec SCP Classification Layers Y Add sig list cundo o Coordinate 922167 1023190 Scale 1 447 598 v Rotation 0 0 v Render EPSG 32616 Fig 20 7 Definition of SCP input for the Landsat image LC80150532014050LGN00 Now we are ready for the creation of ROIs We are going to use the same codes for ROIs in all the Landsat images according to the following table Macroclass name Macroclass ID Built up 1 Vegetation 2 Soil 3 Water 4 About the basics of ROI creation see Create the ROIs page 58 It is possible to create ROIs by drawing manually a polygon using the button ol or with region growing pressing the button and then clicking the map Use the button 2 in ROI creation page 92 for zooming to the polygon extent of the ROI and Show for showing or hiding the temporary ROI With the ROI Creation dock page 89 create as many ROIs as possible assigning a unique Class ID C ID to each ROI and the Macroclass ID MC_ID of the corresponding Macroclass If Display cursor for is checked in the ROI creation page 92 the NDVI value of the pixel beneath the cursor is displayed
77. a function of multispectral bands 6 8 Color Composite 35 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 RGB 543 Fig 6 3 Color composite of a Landsat 8 image Data available from the U S Geological Survey The following weights for I are defined basing on several tests performed using the SCP For Landsat 8 Intensity is calculated as I 0 42 x Blueband 0 98 x Greenband 0 6 x Redband 2 For Landsat 7 Intensity is calculated as I 0 42 x Blueband 0 98 x Greenband 0 6 x Redband NI Rband 3 36 Chapier 6 Basic Definitions Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Fig 6 4 Example of pan sharpening of a Landsat 8 image Left original multispectral bands 30m right pan sharpened bands 15m Data available from the U S Geological Survey 6 9 Pan sharpening 37 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 38 Chapter 6 Basic Definitions CHAPTER 7 Supervised Classification Definitions This chapter provides basic definitions about supervised classifications 7 1 Land Cover Land cover is the material at the ground such as soil vegetation water asphalt etc Fisher and Unwin 2005 Depending on the sensor resolutions the number and kind of land cover classes that can be identified in the image can vary significantly 7 2 Supervised Classification A semi automatic classification also su
78. a point list from text file to the table every line of the text file must contain values separated by tabs of X float Y float MC ID int MC Info text Class ID int C Info text Min size int Max width int Range radius float and optionally the Rapid ROI band int e Export point list export the point list to text file e Create and save ROIs start the ROI creation process for all the points and save ROIs to the Training shapefile Add sig list if checked the spectral signature is calculated the ROI mean value and standard deviation for each raster band and the covariance matrix while the ROI is saved to shapefile it takes some time depending on the number of Input image bands 17 1 2 USGS Spectral Library The tab USGS Spectral Library allows for the download of the USGS spectral library Clark R N Swayze G A Wise R Livo E Hoefen T Kokaly R Sutley S J 2007 USGS digital spectral library splib06a U S Geological Survey Digital Data Series 231 The libraries are grouped in chapters including Minerals Mixtures Coatings Volatiles Man Made Plants Vegetation Communities Mixtures with Vegetation and Microorganisms The downloaded library is automatically sampled according to the image band wavelengths defined in the Band set page 127 and added to the Signature list page 95 Select a chapter Select a chapter select one of the library chapters after the selection chapt
79. a toolbar should be added to QGIS Also a SCP menu is available in the Menu Bar of QGIS It is possible to move the Toolbar page 87 and the docks according to your needs as in the following image Project Edt View Layer Settings Plugins Vector Raster Database Web SCP Processing Hep wy sho A ALS IPAE 2 WHS Add to signature b l J amp 0 010000 Of 60 S 100 6 0 786 0 960 The configuration of available RAM is recommended in order to reduce the processing time From the SCP menu 12 Chapter 2 Installation in Windows 64 bit Semi Automatic Classification Plugin Documentation Release 4 8 0 1 page 85 select Al Settings gt Processing nmm In the Settings page 129 setthe Available RAM MB to a value that should be half of the system RAM For instance if your system has 2GB of RAM set the value to 1024MB I D 2 3 Configuration of the plugin 13 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 14 Chapter 2 Installation in Windows 64 bit CHAPTER 3 Installation in Ubuntu Linux 3 1 QGIS download and installation Open a terminal and type sudo apt get update Press Enter and type the user password Type in a terminal sudo apt get install qgis python matplotlib python scipy Press Enter and wait until the software is downloaded and installed Now QGIS 2 is
80. ad Fig 20 3 Landsat image search 20 2 Download and Pre processing of Landsat images 153 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Semi Automatic Classification Plugin Cr Metadata Satelite AO am _Remove band Fig 20 4 Landsat pre processing Project Edt View Layer Settings Plugins Vector Raster Database Web SCP Processing Help URO ih fie M de b h 9001 0 v BAR 2015 08 11 v m 180160532014057LGN00 2014 02 26 Soe coor s0s32014050LGN00 204021 nz7 1E70150532014090 DC00 20140231 724 53 Fig 20 5 Landsat download 154 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 20 3 Classification of Landsat Images We are going to start the classification of the Landsat 8 image 1C80150532014050LGN00 converted to re flectance Open the directory LC80150532014050LGN00_converted In QGIS open the following bands also with drag and drop RT_LC80150532014050LGN00_B2 tif Blue e RT_LC80150532014050LGN00_B3 tif Green e RT_LC80150532014050LGN00_B4 tif Red RT_LC80150532014050LGN00_BS tif Near Infrared RT LC80150532014050LGNO00 B6 tif Short Wavelength Infrared 1 e RT_LC80150532014050LGN00_B7 tif Short Wavelength Infrared 2 Open the tab Band set page 127 clicking the button in the SCP
81. alc 125 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 17 4 1 Band list Band list isa list of single band rasters loaded in QGIS Fields of Raster bands table Variable automatic variable name for the specific band e g raster1 Band name band name i e the layer name in QGIS Refresh list refresh image list 17 4 2 Expression Enter a mathematical expression for bands In particular NumPy functions can be used with the prefix np e g np logl0 raster1 Fora list of NumPy functions see the NumPy page The expression can work both with Variable and Band name between quotes Also bands in the Band set page 127 can be referenced directly for example bandset b1 refers to band 1 of the Band set Double click on any item in the Band list page 126 for adding its name to the expression Tf text in the Expression is green then the syntax is correct if text is red then the syntax is incorrect and it is not possible to execute the calculation It is possible to enter multiple expressions separated by newlines like in the following example rasterl raster2 raster3 raster4 The above example calculates two new rasters in the output directory with the suffix _1 e g output 1 for the first expression and _ 2 e g output 2 for the second expression The following buttons are available uibs N A F I I I vI F I T p true Es
82. alues It is possible to increase or decrease this value in order to create large or small ROIs Click the button in the ROI creation page 92 and click over the blue area of the map After a few moments the orange semi transparent polygon is displayed over the image In the ROI Signature definition page 93 set MC ID 2 and MC Info Built up also set C 1D 2 and C Info Buildings Create a ROI for the class Veget at ion red areas and a ROI for the class Bare soil green areas following 10 5 Create the ROls 59 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 3 El TISADIID ANS O P Classification Fig 10 5 The ROI saved in the Training shapefile and the corresponding spectral signature displayed in the Signature list Fig 10 6 A temporary ROI created with the automatic region growing algorithm 60 Chapter 10 Tutorial 1 Your First Land Cover Classification Semi Automatic Classification Plugin Documentation Release 4 8 0 1 2 pbeEd P imo SHABNARS beso Do d 3 queers Am MT Select classification algorithm Threshold lae Minimum Distance j 0 0000 al Use Macroclass ID liie i lS export import x E 750 Fig 10 7 The ROI saved in the Training shapefile and the corresponding spectral signature displayed in the Signature list the same steps described previously The following images show a few examp
83. andsat bands home user Desktop LC81910312015006LGN00 Select directory Select MTL file if not in Landsat directory Select a MTL file Brightness temperature in Celsius Apply DOS atmospheric correction Y Use NoData value image has black border 0 Satellite LANDSAT_8 Date YYYY MM DD 2015 01 06 Sunelevation 3 04785914 Earth sundistance 0 9832920 Remove band Band d RADIANCE MULT d RADIANCE ADD REFLECTANCE MULT REF 1 LC81910312015006LGN00_B2 TIF 1 3298E 02 66 48949 2 0000E 05 0 100 2 LC81910312015006LGN00 B3 TIF 1 2254E 02 61 26949 2 0000E 05 0 1000 3 LC81910312015006LGN00_B4 TIF 1 0333E 02 51 66589 2 0000E 05 0 100 4 LC81910312015006LGN00 B5 TIF 6 3234E 03 31 61695 2 0000E 05 0 1000 5 LC81910312015006LGN00_B6 TIF 1 5726E 03 7 86285 2 0000E 05 0 100 6 LC81910312015006LGNO00 B7 TIF 5 3004E 04 2 65020 2 0000E 05 0 1000 A gt gt NS gt _ Create Virtual Raster LJ Create Band set Perform conversion II Show docks GJ Quick user guide Online help Q close Fig 11 6 Landsat conversion to reflectance After a few minutes converted bands are loaded in QGIS 11 3 Clip Data We are going to clip Landsat bands to our study area of course this is optional in case the study is focused on a certain area of the image Download the shapefile of the study area from here Unzip the file and load the shapefile study area Frascati in QGIS Open the tab Cli
84. andsat image conversion to reflectance and DOSI atmospheric correction page 47 for information about the calculation In order to convert automatically Landsat bands to reflectance open the tab Landsat page 114 clicking the button in the SCP menu page 85 or the Toolbar page 87 Click the button Select directory and select the Directory containing Landsat bands i e the directory 1C81910312015006LGN00 The list of bands will be automatically loaded in the table Metadata 66 Chapter 11 Tutorial 2 Land Cover Classification of Landsat Images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 eum 57 Fig 11 2 Downloading Landsat 8 database o Semi Automatic Classification Plugin Qo wo Image list LC81910312015006LGN00 2015 01 06 09 7 79 Fig 11 3 Search Landsat 8 image 11 2 Automatic Conversion to Surface Reflectance 67 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 r Semi Automatic Classification Plugin vo pe 4 5 M 5 M E EL LI Update database iv Select database directory Reset directory m a y MESE a a m SP at 1980 01 01 vw k 2015 06 21 v uE LC81910312015006LGN00 Satellites v Y v Landsat 8 bands E v Download Fig 11 4 Select Landsat 8 bands for download Ur cm NNE A Select database directory TF T v ww eee
85. ass is Cy determinant of the covariance matrix of the data in class Cy 3s inverse of the covariance matrix yy spectral signature vector of class k Therefore TECK gklx gt gj z Vk A j In addition it is possible to define a threshold to the discriminant function in order to exclude pixels below this value from the classification Considering a threshold T the classification condition becomes TECK gklx gt gj z Vk A j and g x gt T Maximun likelihood is one of the most common supervised classifications however the classification process can be slower than Minimum Distance page 40 7 5 Classification Algorithms 41 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 7 5 3 Spectra Angle Mapping The Spectral Angle Mapping calculates the spectral angle between spectral signatures of image pixels and training spectral signatures The spectral angle 0 is defined as Kruse et al 1993 0 r y cos Lia cm T Liar vos Where e x spectral signature vector of an image pixel e y spectral signature vector of a training area e n number of image bands Therefore a pixel belongs to the class having the lowest angle that is r Cy gt O a yp lt 0 z y vk j where e C land cover class k Yp spectral signature of class k e yj spectral signature of class j In order to exclude pixels below this value from the classification it is possible to de
86. ast vrt is created and loaded in QGIS after the conversion Create Band set if checked the Band set is created using converted bands after that Perform conversion is clicked Perform conversion select an output directory and start the conversion process only bands listed in the Metadata table are converted converted Landsat bands are saved in the output directory with the prefix RT_ and automatically loaded in QGIS 17 2 2 Clip multiple rasters Pu Semi Automatic Classification Plugin Vow mem al Pre processing a E Clip coordinates ee le E a ES D NoData value 0 C Ouputnameprek dip Fig 17 9 Clip multiple rasters 116 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 The tab Clip multiple rasters allows for cutting several image bands at once using a rectangle defined with point coordinates or a boundary defined with a shapefile The following video shows this tool http www youtube com watch t 6508v ImbY hilgllg Alternative video link https archive org details video_basic_tutorial_2 start 650 Raster list Refresh list refresh layer list e Select all select all the rasters to be clipped Clip coordinates click the map for the definition of the Upper Left UL and Lower Right LR point coordinates X and Y of the rectangle used for clipping it is possible to enter the coordinates manually
87. ation 4 Bare soil Following the video of this tutorial http www youtube com watch v Imb Yhilgl 1g Alternative video link https archive org details video_basic_tutorial_2 11 1 Data Download We are going to download the Landsat 8 image using the SCP tool Download Landsat page 108 The dataset we are going to download is a Landsat 8 image that includes the metadata file the file LC81910312015006LGNO0_MTL txt and the following Landsat 8 bands for more information read Landsat Satellite page 34 e LC81910312015006LGNO00 B2 tif Blue e LC81910312015006LGNO00 B3 tif Green e LC81910312015006LGNO00 B4 tif Red e LC81910312015006LGNO00_BS tif Near Infrared e LC81910312015006LGNO00 B6 tif Short Wavelength Infrared 1 e LC81910312015006LGNO00 B7 tif Short Wavelength Infrared 2 Landsat images are available from the U S Geological Survey and these bands are downloaded through the Amazon Web Services Start a new QGIS project Open the tab Download Landsat page 108 clicking the button E in the SCP menu page 85 or the Toolbar page 87 First we need to download the Landsat database Click the button Select database directory in order to define where to save the database It is preferable to create a new directory e g LandsatDB in the user directory Check the option only Landsat 8 in order to download the database of Landsat 8 only Click the button Update database and click Yes in the following question
88. ation 1 0 Unclassified Show 1 Built up 5 T ra 2 Ni 2 Vegetation y Display cursor for NOVI v DOS 3 Soll beeen L 4 Water Ls de o 1C80150532014050L GNOO MC ID MC info i e 0 376693 1 C Macroclass 1 U PY RT_LC8015053201 40501 GNOO cip C info 2 Mo 1 Ol class 1 SCP Classification Layers e di y Add sig list Mondo ES Coordinate 926610 995150 Scale 1 1 517 856 v Rotation 0 0 v Render QEPSG 32616 Fig 20 27 Classification 3 refined with NDVI Now that the classification of vegetation has been enhanced for the three images we are going to mask clouds and border pixels in order to avoid classification errors 20 5 Cloud Masking Landsat 8 images include Quality Assessment bands QA that are useful for identifying clouds Pixel val ues Of QA bands are codes that represent combinations of surface and atmosphere conditions These val ues indicate with high confidence cirrus or clouds pixels for the description of these codes see the table at http landsat usgs gov L8 Quality AssessmentBand php The QA band of the Landsat 8 image LC80150532014050LGNO00 includes mainly the values 53248 and 61440 indicating clouds and the value 36864 indicating potential clouds Therefore we are going to write an expression that masks our classification i e classification_1_NDVI where pixels of the QA band are equal to one of these values In QGIS open the band LC80150532014050LGNO0 BQA that is inside the d
89. ation Result The result of the classification process is a raster see an example of Landsat classification in Figure Landsat classification page 44 where pixel values correspond to class IDs and each color represent a land cover class Fig 7 2 Landsat classification Data available from the U S Geological Survey A certain amount of errors can occur in the land cover classification i e pixels assigned to a wrong land cover class due to spectral similarity of classes or wrong class definition during the ROI collection 7 8 Accuracy Assessment After the classification process it is useful to assess the accuracy of land cover classification in order to identify and measure map errors Usually accuracy assessment is performed with the calculation of an error matrix which is a table that compares map information with reference data 1 e ground truth data for a number of sample areas Congalton and Green 2009 The following table is a scheme of error matrix where k is the number of classes identified in the land cover classification and n is the total number of collected sample units The items in the major diagonal aii are the number of samples correctly identified while the other items are classification error Ground truth 1 Groundtruth2 Ground truth k Total Class 1 411 012 bia Alk Q1 Class 2 0491 022 a 092k a24 Class k Qk1 Ak2 d Akk k Total a41 a42 m Q Lk n There
90. cessing Bandcalc me Bandset A Settings P About 3 Multiple ROI creation USGS Spectral Library f Algorithm band weight il Signature threshold 2 Download Landsat Database x Update database only Landat 8 Select database directory Area coordinate s IL UL X Lon Y Lat X Lon Y Lat E Acqusition date from 1980 01 01 v to 2015 06 21 v Max cloud cover 100 gt y ID Satellites Y 4 5 TM Y 7 ETM v 8 OLI Find images Landsat images Download options Y Band 1 Y Band 2 Y Band 3 Y Band 4 Y Band 5 v Band 6 Y Band 7 iV Band 8 Panchromatic Band 9 Y Band 10 iv Band 11 Y Band QA Check uncheck all bands Exportlinks Download images from list W only if preview in Layers Vv Pre process images Load bands in QGIS Show docks GJ Quick user guide Online help Q Close Fig 17 6 Download options Landsat 8 bands Bands Only checked bands are downloaded if the image is provided by the Amazon Web Services Check uncheck all bands select or deselect all Landsat 8 bands Download It is possible to download multiple images i e all the images in the image list table and select which bands to download for each image During the download it is recommended not to interact with QGIS e Export links export the download links to a text file e Download images from list start the download process of all the images listed i
91. cover change statistics are displayed in the tab frame and the land cover change raster is loaded in QGIS 17 3 3 Classification report The tab Classification report allows for the calculation of class statistics as number of pixels percent age and area area unit is defined from the image itself The following video shows this tool http www youtube com watch t 3070 amp v acxmIrM Qns Alternative video link https archive org details video_tutorial_Landsat_mosaic_ENG start 3070 Classification input Select the classification select a classification raster already loaded in QGIS e Refresh list refresh layer list Use NoData value if checked NoData value will be excluded from the report e Calculate classification report calculate the report and display it in the tab frame e Save report to file save the report to a csv file 17 3 4 Classification to vector The Classification to vector allows for the conversion of a classification to shapefile Classification input Select the classification select a classification raster already loaded in QGIS e Refresh list refresh layer list Use code from Signature list if checked color and class information are defined from Signa ture list page 95 according to the selection between MC ID and C ID in the combobox 17 3 Post processing 121 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Semi Automatic Class
92. cover classification of the whole image In the Classification output page 99 click the button Perform classification and define the name of the classification output The classification output is a raster file tif where each pixel value corresponds to a land cover class defined in the Signature list page 95 Well done You have just performed your first land cover classification However you can see that there are several classification errors especially soil classified as built up and vice versa because the number of ROIs spectral signatures is insufficient In the following Tutorial 2 Land Cover Classification of Landsat Images page 65 we are going to create more ROIs and improve the classification results 10 6 Create a Classification Preview 61 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Fig 10 8 Vegetation 62 Chapter 10 Tutorial 1 Your First Land Cover Classification Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Fig 10 9 Bare soil TIP The region growing algorithm can create more homogeneous spectral signatures than ROI created manually which is good for the use of the algorithm Spectral Angel Mapping and Maximum Likelihood The manual creation of ROIs can be useful in order to account for the spectral variability of a class especially when using the algorithm Maximum Likelihood Fig 10 10 Definition of class colors in the table Signatu
93. ddition to the classification output save the intermedi ate files calculated by the classification algorithm one tif file for each land cover class representing the similarity of each pixel to the class thereof e Use virtual rasters for temp files Q if checked create virtual rasters for certain tempo rary files instead of creating real rasters it is useful for reducing disk space usage during calculations e Raster compression Q if checked a lossless compression DEFLATE OR PACKBITS is applied to raster outputs in order to save disk space however using raster compression sometimes can produce files larger than rasters without compression Image calculation Raster data type for image calculations Q select the raster data type between Float32 default and Float64 which is used for the creation of raster outputs RAM e Available RAM MB gt QJ set the available RAM in MB that will be used during the processes this value should be half of the system RAM e g 1024MB if system has 2GB of RAM Temporary directory e temporary directory Q path to the temporary directory e Change directory select a new temporary directory where temporary files are saved during pro cessing Reset to default reset to system default temporary directory 17 6 3 Settings Debug Debugging utilities for the creation of a Log file i e recording of SCP activities for reporting issues and testing SCP dependencies
94. der to find similar spectral signatures and delete them Highlight with mouse selection in the table two or more spectral signatures in the Signature list page 95 then click the button bX The Spectral Signature Plot page 135 is displayed in a new window In this window you can see the spectral Plot page 136 of signatures the Signature details page 138 and assess Spectral distances page 138 Move inside the Plot page 136 and see if signatures are similar i e very close or dissimilar i e not very close Y Calcul 1 Water 1 Lake 24Built up 24Buildings 3 Vegetation 3 Vegetation we as 1s 17 E Wavelength um 1 E 6m oo Bar pantoom E Spectral distances 11 9 Create the Classification Output Repeat iteratively the phases Create the ROIs page 73 and Create a Classification Preview page 78 until the classification previews are good In order to create a classification output using only the Macroclass ID defined in Create the ROIs page 73 activate the checkbox Use Macroclass ID in Classification algorithm page 97 In order to classify the entire image in the Classification output page 99 click the button Perform classification and define the name of the classification output You can notice that the resulting classification is better than the one created in Tutorial 1 Your First Land Cover Classification page 57 However there are other tools and techn
95. dock page 95 Fo SCP Spectral Signature Plot Y c y Fig 18 1 Spectral Signature Plot The following video shows this tool http www youtube com watch t 900 amp v acxmIrM Qns 135 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Alternative video link https archive org details video_tutorial_Landsat_mosaic_ENG start 900 18 1 Plot Signature list Table fields S checkbox field if checked then signature is displayed in the plot MC ID Macroclass ID MC Info Macroclass Information Class ID Class ID C Info Class Information Color color field double click to select a color for the plot e Plot a if checked plot the standard deviation for each signature Remove signatures remove highlighted signatures from this list e Fit to data resize the plot to fit to all the data e Calculate spectral distances if checked calculate the following spectral distances Jeffries Matusita Distance page 42 Spectral Angle page 43 Euclidean Distance page 43 Bray Curtis Similarity page 43 18 1 1 Plot It is possible to move the legend inside the plot with the mouse Plot commands from Matplotlib gt MA Reset to original view Back to previous view Forward to next view Pan axes with left mouse zoom with right Zoom to rectangle Unused Save plot to a figure e g JPG file SN 00 Unus
96. dsat 8 image Project Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help DmBE OXE RENE teni se gt gt vos ES 232 vis Son S EF BB COO fh fh n aye SCP Classification 9 x SCP ROI creation gt x 5 gt Signature a Training shapefile V _Open Save Reset ROI viu Newshp d Si LUCAN M S Civi MCinfo CID Cinfo Color MCID MCifo Kv C Info fa itt MB ol Ja 353 le 9 d Ve Select classification algorithm Threshold Max ROT width e t Maximum Likelihood w 0 0000 gf 0 006000 60 100 C aaa Y Rapid ROI on band 19 2 4 _ Automatic refresh ROI Automatic plot a Sri Qi i 1 Redos Show CO ti mos ER o P d Y Display cursor for NDVI v 1 Ca E me RO Signature definition ha ala A 1 C Built up n T __ create vector __ Classification report CiD nto Perform classification 1 i Built up1 E SCP Classification Layers SaveRO Y Add sig list und 5 Coordinate 943878 1242134 Scale 1 517 858 v Rotation 0 0 CO v Render EPSG 32616 Fig 20 22 Definition of SCP input for the Landsat image LE70150532014090EDC00 Again create a land cover classification following the steps previously described Now we have 3 land cover classifications that we can enhance in several ways 20 4 Enhancement of Classification Using NDVI We are going to calculate NDVI for
97. e 4 8 0 1 Project Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help 6288 NF En YE e o A A EE ADO d del 8856 gt Layers vo SCP ROI creation e x e Vo ntaa Training shapefile v E dassification 1 NOVI 9 New shp E a O Undassfied L M 2 DUE UD MCiD MCifo CID C info 2 Vegetation 3 Soil Bi 4 water v B Novi Ltif F dassification_1 0 Unclassified 1 Built up 2 Vegetation 3 Soil 4 Water V BP Rr c80150532014050L GNOO Add to signature ESI EL F Range radius Min ROI size Max ROI width DOC 1 18 t o 0 010000 60 100 0 376693 Rapid ROI on band 10 E IF RT 1C80150532014050L GNOO ze 0 Automatic refresh ROI Automatic plot 0 529091 E EF RT_LC8015053201 40501 GNOO Jv E Mo j Die Show es 0 793 xiv de c Bf RT LC80150532014050L GNOO y Display cursor for NDVI_v m a o rs a 0 7 RO Signature definition x CH WM RT 1c80150532014050L GNOO MCI MC info v 5 o 1 C Macroclass 1 m 0 659 ci C info d EF RT_L C801505320140501 GNOO rey v 15M Class 1 Lal _SCP Classification Layers Y Add sig list y Coordinate 899952 999060 Scale 1 1 447 598 v Rotation 0 0 y Render EPSG 32616 Fig 20 25 Classification 1 refined with NDVI Now we perform the same enhancement for the other land cover classifications For the Landsat 8 image 1C80160532014
98. e Spectral Signature Plot page 135 i open the Scatter Plot page 141 85 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 E open the Tools page 102 and the sub menu thereof L open the Pre processing page 114 and the sub menu thereof r open the Post processing page 117 and the sub menu thereof open the Band calc page 125 Y E open the Settings page 129 and the sub menu thereof A open the online user manual in a web browser open the Online help in a web browser also a Facebook group and a Google Community are available for sharing information and asking for help about SCP 86 Chapter 13 SCP menu CHAPTER 14 Toolbar EN put mago A A AE ON AA E pl E Fig 14 1 Toolbar The Toolbar allows for the selection of the Input image and includes several buttons for opening the main functions of the Main Interface Window page 101 The following video shows this tool http www youtube com watch t 140 amp v nZffzX_sMnk Alternative video link https archive org details video_basic_tutorial_1 start 140 P Configuration stored in the active project of QGIS Q Configuration stored in QGIS registry E show the Main Interface Window page 101 and display the ROI Creation dock page 89 and the Classification dock page 95 open the Band set page 127 Input image P select the input image from a list of
99. e Training Shapefile and Signature List File page 71 we have loaded the Signature list file created in Tutorial 1 Your First Land Cover Classifi cation page 57 without defining the center wavelength of each band We need to delete the signatures created in Tutorial 1 Your First Land Cover Classification page 57 from the Signature list file page 95 highlight with mouse selection in the table these signatures and click the button Ne Then highlight with mouse selection in the table the corresponding ROIs in the ROT list page 91 and click the button Add to signature The spectral signatures will be calculated with the correct center wavelength and added to the Signature list file page 95 11 8 Assess Spectral Signatures The classification algorithm uses spectral signatures for classifying the image In general one should use spectral signatures that are not similar in order to avoid classification errors Therefore it is useful to assess signatures in 78 Chapter 11 Tutorial 2 Land Cover Classification of Landsat Images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 la LA E NL mie mae amp SB la S T Len 2 CEE V y lef Spectral Angle Mapping v Use Macrocass iD Le Show size 100 Madow e classification Style Select gmi classification output ig Apply mask Create vector Classification Jassiication Fig 11 24 Warning 9 or
100. e details Spectral distances 1 gt 74 16 Ta DAR e x 1 94664 y 24201 2 mena Boy Y QGIS 2 8 2 Wien JF sce Spectra Sio Om Semi Automatic OS is free software you can redistribute it and or modify it under the terms of the GNU General Public License as published by the Free Software Foundation version 3 of the License Semi Automatic OS is distributed in the hope that it will be useful but WITHOUT ANY WARRANTY without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE See the GNU General Public License for more details See http www gnu org licenses 195 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 196 Chapter 23 Installation in VirtualBox Part VII Frequently Asked Questions 197 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Plugin installation page 201 How to install the plugin manually page 201 Pre processing page 203 Which image bands should I use for a semi automatic classification page 203 Which Landsat bands can be converted to reflectance by the SCP page 203 Can I apply the Landsat conversion and DOS correction to clipped bands page 203 Can I apply the DOS correction to Landsat bands with black border i e with NoData value page 203 How to remove cloud cover from Landsat images page 204 How do I create a virtual raster manually in QGIS page 204
101. e following table Macroclass name Macroclass ID Class name Class ID Vegetation 1 Grass 1 Vegetation 1 Trees 2 Built up 2 Road 3 39 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Therefore Classes are subsets of a Macroclass as illustrated in Figure Macroclass example page 40 Macroclass 1 Vegetation Class 2 Trees Class 1 Grass Fig 7 1 Macroclass example If the use of Macroclass is not required for the study purpose then the same Macroclass ID can be defined for all the ROIs e g Macroclass ID 1 and Macroclass values are ignored in the classification process 7 5 Classification Algorithms The spectral signatures spectral characteristics of reference land cover classes are calculated considering the values of pixels under each ROI having the same Class ID or Macroclass ID Therefore the classification algo rithm classifies the whole image by comparing the spectral characteristics of each pixel to the spectral character istics of reference land cover classes SCP implements the following classification algorithms 7 5 1 Minimum Distance Minimum Distance algorithm calculates the Euclidean distance d x y between spectral signatures of image pixels and training spectral signatures according to the following equation where e x spectral signature vector of an image pixel e y spectral signature vector of a training area 40
102. e is created The recommended size of the hard drive is 10 00 GB Do not add a virtual hard drive Create a virtual hard drive now Semi Automatic OS vmdk Normal 10 00 C 1j lt Back Create Cancel 5 Start the Semi Automatic OS by clicking the Start button 6 It is recommended to install the virtualbox guest utils in the virtual machine from the Menu gt Preferences Synaptic Package Manager it allows for a better integration of the Semi Automatic OS in the host system such as the resize of the system window or the folder sharing The Semi Automatic OS includes a sample dataset Landsat 8 images available from the U S Geological Survey that can be used for testing purposes 194 Chapter 23 Installation in VirtualBox Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Nds 2 8 24Nien me pem e qm Vector Raster Database Web SCP Proce SER aloe AP DD s ae EB IE 7 899 A ICE EE lea Sr NED Vegetation 1 Vegetation Built up 2 Built up 1 Vegetation 1 Vegetation Built up 2 Built up Range radius Min ROI size 1000 0000 60 aos E T Rapid ROI on band C Automatic refresh ROI C Automatic e RoIcreation mo mi Ai EU Show ROI Cho 22 Buitup y va Save ROI F Add sig list Undo BJ coordinate 297238 4632925 Scale 1 79 159 y Rotation o o 3 F Render EPSG 32633 Rool Signatur
103. ease 4 8 0 1 6 3 Sensors Sensors can be on board of airplanes or on board of satellites measuring the electromagnetic radiation at specific ranges usually called bands As a result the measures are quantized and converted into a digital image where each picture elements i e pixel has a discrete value in units of Digital Number DN NASA 2013 The resulting images have different characteristics resolutions depending on the sensor There are several kinds of resolutions Spatial resolution usually measured in pixel size is the resolving power of an instrument needed for the discrimination of features and is based on detector size focal length and sensor altitude NASA 2013 spatial resolution is also referred to as geometric resolution or IFOV Spectral resolution is the number and location in the electromagnetic spectrum defined by two wave lengths of the spectral bands NASA 2013 in multispectral sensors for each band corresponds an image Radiometric resolution usually measured in bits binary digits is the range of available brightness values which in the image correspond to the maximum range of DNs for example an image with 8 bit resolution has 256 levels of brightness Richards and Jia 2006 For satellites sensors there is also the temporal resolution which is the time required for revisiting the same area of the Earth NASA 2013 6 4 Radiance and Reflectance Sensors measure the radiance whic
104. ed 136 Chapter 18 Spectral Signature Plot Semi Automatic Classification Plugin Documentation Release 4 8 0 1 3 Vegetation 3 Trees 2 Built up 2 Built up1 Fig 18 2 Spectral Signature Example of spectral signature plot SCP Spectral Signature Plot Fig 18 3 Spectral Signature Signature details 18 1 Plot Signature list 137 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 18 1 2 Signature details Display the details about spectral signatures i e Wavelength Values and Standard deviation This is useful for comparing the values of different spectral signatures or pixels Signature details x MC ID 3 MC info Vegetation C ID 3C info Trees Wavelength E 6m 0 48 0 56 0 655 0 865 1 61 2 2 Values 0 09486 0 08931 0 05692 0 46409 0 18053 0 08169 Standard deviation 0 00225 0 00309 0 00505 0 03453 0 01167 0 00797 MC ID 2 MC info Built up C ID 2C info Built upl Wavelength E 6m 0 48 0 56 0 655 0 865 1 61 2 2 Values 0 13733 0 12959 0 14341 0 25043 0 24046 0 19806 Standard deviation 0 01193 0 01334 0 01754 0 01941 0 02057 0 02378 Fig 18 4 Spectral Signature Example of signature details 18 1 3 Spectral distances Display spectral distances for each combination of signatures if Calculate spectral distances is checked in Plot Signature list page 136 It i
105. ed by comma or semicolon e g LC81910312015006LGNO0 LC81910312013224LGN00 Satellites search only the databases of the Landsat satellites checked here deselecting unwanted satellites can make the search faster Find images start searching Landsat images the search can last a few minutes depending on the settings thereof results are displayed inside the table in Landsat images page 110 Landsat images Image list This table displays the results of the Landsat search Table fields ImagelD the Landsat Image ID AcquisitionDate date of acquisition of Landsat image CloudCover percentage of cloud cover in the image Path path of the image Row row of the image min lat minimum latitude of the image min lon minimum longitude of the image max lat maximum latitude of the image max lon maximum longitude of the image Service download service of the image Preview URL of the image preview Display image preview display image preview of highlighted images in the map preview are roughly georeferenced on the fly Remove images from list remove highlighted images from the list Clear table remove all images from the list 110 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Download options E y Semi Automatic Classification Plugin EF Tools E Pre processing a Post pro
106. ed to create Training shapefile and Signature list file in order to collect Training Areas page 39 ROIs and calculate the Spectral Signature page 33 thereof for very basic definitions see Tutorial 1 Your First Land Cover Classification page 57 In the ROI Creation dock page 89 click the button New shp and define a name e g ROI shp in order to create the Training shapefile that will store ROI polygons The shapefile is created and added to QGIS The name of the Training shapefile is displayed in Training shapefile page 89 Also click the button Save in the Classification dock page 95 and define a name e g SIG xm1 in order to create the Signature list file that will store spectral signatures The path of the Signature list file is displayed in Signature list file page 95 nee Edt View Layer Settings Plugins Vector Raster Database Web SCP Processing Help BE QF a tert set gt 0 00 3 aa A AS Be ARO fle oie 8 6 SCP Classification x SCP ROI creation th o E D z rainin 1 WB o Save Reset ROLshp JLO Newshp e CLL E E S MCID MCinfo CID Cinfo Color MC ID MC Info cio C info be p B 3 ENT p la oi Le s t Export Import Add to signature DMA FY classification algorithm RO parameter a VC Sl cancion startin Threshold Range radius Min ROI size Max ROI width Minimum Distance 0 0000 M 0 010000 60 100 7 _ Use Macroclass ID Classification preview _
107. efine a multiplicative rescaling factor and additive rescaling factor for each band for instance using the values in Landsat metadata which allow for on the fly conversion to TOA while calculating spectral signatures or classifying Table fields Band name P name of the band this element cannot be edited Center wavelength P center of the wavelength of the band enter a value Multiplicative Factor P multiplicative rescaling factor enter a value Additive Factor P additive rescaling factor enter a value Wavelength unit P select the wavelength unit among Band number no unit only band number um micrometres nm nanometres Control bands f move highlighted bands upward Sort by name sort automatically bands by name giving priority to the ending numbers of name move highlighted bands downward Remove band remove highlighted bands from the band set I Clear all clear all bands from band set Import import a previously saved band set from file ml Export export the band set to a file Quick wavelength settings optional rapid definition of band center wavelength for the following satellite se GeoEye 1 Landsat 8 OLI Landsat 7 ETM Landsat 5 TM Landsat 4 TM 128 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Landsat 1 2 and 3 MSS
108. emi Automatic Classification Plugin Documentation Release 4 8 0 1 SCP Classification PORTER Classification algorithm Classification preview Fig 16 1 Classification dock Eur ure list file Fig 16 2 Signature list file 96 Chapter 16 Classification dock Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Signature list S MCID MC Info CID Cinfo Color La E b i M ls Export Import Fig 16 3 Signature list MC Info signature Macroclass Information text it can be edited with a single click C ID signature Class ID int it can be edited with a single click C Info signature Class Information text it can be edited with a single click Color color field double click to select a color for the class that is used in the classification Ne delete highlighted spectral signatures from the list JA merge highlighted spectral signatures obtaining a new signature calculated as the average of signature values for each band covariance matrix is excluded bX add highlighted signatures to the Spectral Signature Plot page 135 Kall import a spectral library from ASTER spectral libraries i e files txt downloaded from http speclib jpl nasa gov USGS spectral libraries i e files asc downloaded from http speclab cr usgs gov spectral lib html or generic csv files i open the USGS Spectral Library page 104 for importing USGS
109. entation Release 4 8 0 1 Semi Automatic Classification Plugin Temporary directory Fig 20 1 SCP settings 20 2 Download and Pre processing of Landsat images 151 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Ep Ww Semi Automatic Classification Plugin Y O Q9 EF Tools of Pre processing E Postprocessing lt lt Band calc am Band set Settings 7 About Ei Muttiple ROI creation ly USGS Spectral Library f Algorithm band weight ll Signature threshold p Download Landsat Database Coe ere Update database only Landat 8 Area coordinate s Image list EE d SSS SSS EE em Download Fig 20 2 Landsat database download With SCP it is possible to find an image basing on the ID thereof using the Search page 110 options In particular in Image ID paste the following IDs and click Find images LC80150532014050LGN00 LC80160532014057LGN00 LE70150532014090EDCO00 After a few seconds the three images are listed in the Landsat images page 110 Before downloading the images we need to define the options for the conversion to reflectance which will be performed automatically to downloaded images Open the tab Landsat page 114 clicking the but ton Y in the Toolbar page 87 enable the options Apply DOS1 atmospheric correction and Brightness temperature in Ce
110. eparability of materials During the classification process the spectral signature values and the corresponding band weights are multiplied thus modifying the spectral distances Band weight Table fields Band number number of the band in the band set 106 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Band name name of the band Weight weight of the band it can be edited directly or through the button Set weight value e Weight value value of weight used with the button Set weight value e Set weight value set the value defined in Weight value for all the highlighted bands in the table e Reset weights reset all band weights to 1 17 1 4 Signature threshold E 49 Semi Automatic Classification Plugin Fig 17 4 Signature threshold The tab Signature threshold allows for the definition of a classification threshold for each spectral sig nature This is useful for improving the classification results especially when spectral signatures are similar Thresholds of signatures are saved in the Signature list file page 95 Tf threshold is O then no threshold is applied Depending on the selected Classification algorithm page 97 the threshold value is considered differently for Minimum Distance pixels are unclassified if distance is greater than threshold value for Maximum Likelihood pixels are unclassified if
111. er EPSG 4326 Q 5 2 Semi Automatic Classification Plugin installation Run QGIS 2 From the main menu select Plugins Manage and Install Plugins Project Edit View Layer Settings Plugins Vector Raster Help i E Bu j 3 Tele a A e 6 Q X i e e Python Console Ej mm 0 From the menu A11 select the Semi Automatic Classification Plugin and click the button Install plugin 23 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Semi Automatic Classification Plugin Plugin for the semi automatic supervised classification designed to expedite the ig of multispectral or hyperspectral remote sensing images which provides a set of tools for pre processing and post processing Written by Luca Congedo the Semi Automatic Classification Plugin SCP lea fee for the semi automatic supervised classification of remote sensing eames Providing tools to expedite the creation of ROIs training areas inu rene region growing or multiple ROI creation The spectral me of training areas ca automat id di 1 isplayed in a spedral n signature plot It is possible to import sp signatures from eem T sources Also a tool allows for the selection and download of spere TE Signatures E the USGS Spectral Library http spedab cr usgs gov spectral lib html Several tools are available for iM 20m andbedeckung Classificazione della Copertura ra del Suolo Forn more e formation please v
112. er libraries are shown in Select a library page 106 104 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Po Semi Automatic Classification Plugin Y e Select a library Fig 17 2 USGS Spectral Library 17 1 Tools 105 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Select a library Select a library select one of the libraries the library description is displayed in the frame Library description Add to signature list internet connection required download the library and add the sampled spectral signature to the Signature list page 95 using the parameters defined in ROT Signature definition page 93 Tip spectral libraries downloaded from the USGS Spectral Library can be used with Minimum Distance or Spectral Angle Mapping algorithms but not Maximum Likelihood be cause this algorithm needs the covariance matrix that is not included in the spectral libraries 17 1 3 Algorithm band weight FO Semi Automatic Classification Plugin cw EF Tools of Preprocessing le Post processing Band calc mm Band set settings 7 About Ki Multiple Ror creation jy USGS Spectral Library 4 Algorithm band weight al Signature threshold Download Landsat Band weight ER Fig 17 3 Algorithm band weight The tab Algorithm band weight allows for the definition of band weights that are useful for improving the spectral s
113. f images the post processing of classifications and the raster calculation SCP allows for the rapid creation of ROIs training areas through region growing algorithm which are stored in a shapefile The scatter plot or ROIs is available Spectral signatures of training areas are calculated automati cally and can be displayed in a spectral signature plot along with the values thereof Spectral distances among signatures e g Jeffries Matusita distance or spectral angle can be calculated for assessing spectral separability Spectral signatures can be exported and imported from external sources Also a tool allows for the selection and download of spectral signatures from the USGS Spectral Library SCP implements a tool for searching and downloading Landsat and Sentinel images The following tools are available for the pre processing of images automatic Landsat conversion to surface reflectance clipping multiple rasters and splitting multi band rasters The classification algorithms available are Minimum Distance Maximum Likelihood Spectral Angle Mapping SCP allows for interactive preview of classification The post processing tools include accuracy assessment land cover change classification report classification to vector reclassification of raster values Also a band calc tool allows for the raster calculation using NumPy functions For more information and tutorials visit the official site From GIS to Remote Sensing How
114. fic additive rescaling factor from Landsat metadata RADIANCE ADD BAND x where x is the band number Qcal Quantized and calibrated standard product pixel values DN 8 2 Top Of Atmosphere TOA Reflectance For relatively clear Landsat scenes a reduction in between scene variability can be achieved through a nor malization for solar irradiance by converting spectral radiance as calculated above to planetary reflectance or albedo This combined surface and atmospheric reflectance of the Earth is computed with the following for mula NASA 2011 p 119 Pp n Ly d ESU Ny cos0s where py Unitless TOA reflectance which is the ratio of reflected versus total power energy NASA 2011 p 4T e Ly Spectral radiance at the sensor s aperture at satellite radiance 47 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 d Earth Sun distance in astronomical units provided with Landsat 8 metafile and an excel file is available from http landsathandbook gsfc nasa gov excel_docs d xls ESUN Mean solar exo atmospheric irradiances 0 Solar zenith angle in degrees which is equal to 0 90 6 where 0 is the Sun elevation It is worth pointing out that Landsat 8 images are provided with band specific rescaling factors that allow for the direct conversion from DN to TOA reflectance However the effects of the atmosphere i e a disturbance on the reflectance that varies wit
115. fine a threshold T TEO e Olx yk 0 r y Vk j and Spectral Angle Mapping is largely used especially with hyperspectral data 7 6 Spectral Distance It is useful to evaluate the spectral distance or separability between training signatures or pixels in order to assess if different classes that are too similar could cause classification errors The SCP implements the following algorithms for assessing similarity of spectral signatures 7 6 1 Jeffries Matusita Distance Jeffries Matusita Distance calculates the separability of a pair of probability distributions This can be particularly meaningful for evaluating the results of Maximum Likelihood page 41 classifications The Jeffries Matusita Distance J is calculated as Richards and Jia 2006 dug 1 Erta 1 EA B Ej LE 2 je E 01 P ris x first spectral signature vector where where e y second spectral signature vector 42 Chapter 7 Supervised Classification Definitions Semi Automatic Classification Plugin Documentation Release 4 8 0 1 e Y covariance matrix of sample x X covariance matrix of sample y The Jeffries Matusita Distance is asymptotic to 2 when signatures are completely different and tends to 0 when signatures are identical 7 6 2 Spectral Angle The Spectral Angle is the most appropriate for assessing the Spectra Angle Mapping page 42 algorithm The spectral angle 0 is defined as Kruse et al
116. fore it is possible to calculate the overall accuracy as the ratio between the number of samples that are correctly classified the sum of the major diagonal and the total number of sample units n Congalton and Green 2009 For further information the following documentation is freely available Landsat 7 Science Data User s Hand book Remote Sensing Note or Wikipedia References Congalton R and Green K 2009 Assessing the Accuracy of Remotely Sensed Data Principles and Practices Boca Raton FL CRC Press e ESA 2015 Sentinel 2 User Handbook Available at https sentinel esa int documents 247904 685211 Sentinel 2 User Handbook Fisher P F and Unwin D J eds 2005 Representing GIS Chichester England John Wiley amp Sons e JARS 1993 Remote Sensing Note Japan Association on Remote Sensing Available at http www jars1974 net pdf rsnote_e html 44 Chapter 7 Supervised Classification Definitions Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Johnson B A Tateishi R and Hoan N T 2012 Satellite Image Pansharpening Using a Hybrid Approach for Object Based Image Analysis ISPRS International Journal of Geo Information 1 228 Available at http www mdpi com 2220 9964 1 3 228 Kruse F A et al 1993 The Spectral Image Processing System SIPS Interactive Visualization and Analysis of Imaging spectrometer Data Remote Sensing of Environment NASA 2013 Landsat
117. gend Max number of characters gt limit the text length of names in the Plot Signa ture list page 136 legend 17 6 Settings 129 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Pu Semi Automatic Classification Plugin Vow shape inc Spectral signature Variable name for expressions tab Reclassification raster Reset Tempor ary group name Class temp group _ Resetname Fig 17 18 Settings Interface 130 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Variable name for expressions tab Reclassification Set the variable name used in Old value expressions of the Reclassification page 124 Variable name Q set variable name default is raster Reset name reset variable name to default Temporary group name Set the temporary group name in QGIS Layers used for Classification preview page 98 e Group name Q set group name default is Class temp group Reset name reset group name to default 17 6 2 Settings Processing Semi Automatic Classification Plugin Temporary director d Fig 17 19 Settings Processing Classification process e Play sound when finished Q if checked play a sound when the classification process is com pleted 17 6 Settings 131 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Save algorithm files Q if checked in a
118. h corresponds to the brightness in a given direction toward the sensor it useful to define also the reflectance as the ratio of reflected versus total power energy 6 5 Spectral Signature The spectral signature is the reflectance as a function of wavelength see Figure Spectral Reflectance Curves of Four Different Targets page 33 each material has a unique signature therefore it can be used for material classification NASA 2013 60 4 a Pinewoods Grasslands 20 Red Sand Pit Percent Reflectance Silty Water 0 4 0 6 0 8 1 0 1 2 Wavelength m Fig 6 2 Spectral Reflectance Curves of Four Different Targets from NASA 2013 6 3 Sensors 33 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 6 6 Landsat Satellite Landsat is a set of multispectral satellites developed by the NASA National Aeronautics and Space Administra tion of USA since the early 1970 s Landsat images are very used for environmental research The resolutions of Landsat 4 and Landsat 5 sensors are reported in the following table from http landsat usgs gov band_designations_landsat_satellites php also Landsat temporal resolution is 16 days NASA 2013 Landsat 4 Landsat 5 Bands Wavelength micrometers Resolution meters Band 1 Blue 0 45 0 52 30 Band 2 Green 0 52 0 60 30 Band 3 Red 0 63 0 69 30 Band 4 Near Infrared NIR 0 76 0 90 30 Band 5 SWIR
119. h the wavelength should be considered in order to measure the reflectance at the ground 8 3 Surface Reflectance As described by Moran et al 1992 the land surface reflectance p is p n Ly Lp d T ESUN coss T Edown where Ly is the path radiance T is the atmospheric transmittance in the viewing direction T is the atmospheric transmittance in the illumination direction e Edown is the downwelling diffuse irradiance Therefore we need several atmospheric measurements in order to calculate p physically based corrections Alternatively it is possible to use image based techniques for the calculation of these parameters without in situ measurements during image acquisition It is worth mentioning that Landsat Surface Reflectance High Level Data Products for Landsat 8 are available for more information read http landsat usgs gov CDR_LSR php 8 4 DOS1 Correction The Dark Object Subtraction DOS is a family of image based atmospheric corrections Chavez 1996 explains that the basic assumption is that within the image some pixels are in complete shadow and their radiances received at the satellite are due to atmospheric scattering path radiance This assumption is combined with the fact that very few targets on the Earth s surface are absolute black so an assumed one percent minimum reflectance is better than zero percent It is worth pointing out that the accuracy of image based techniques i
120. he table at http landsat usgs gov L8 Quality AssessmentBand php 61440 59424 e 57344 56320 e 53248 31744 28672 In particular the Quality Assessment Band of the sample dataset includes mainly the value 53248 indicating clouds Therefore in this tutorial we are going to exclude the pixels with the value 53248 from all the Landsat bands Following the video of this tutorial http www youtube com watch v vjKX00 ML64 Alternative video link https archive org details video_band_calc First download the sample dataset which is a Landsat 8 image already converted to reflectance see Automatic Conversion to Surface Reflectance page 66 from this link data available from the U S Geological Survey Also download the land cover classification from here 21 1 Application of a mask to multiple bands Unzip the downloaded dataset and load all the raster bands in QGIS Open the Band calc page 125 and click the button Refresh list We are going to use conditional expressions i e np where for more information see this page with the following structure 179 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 IP RI LCOI10312015006LGN00 62 Wo 0202136 L 00799775 Y Lcmo10312015000 609 8QA 20480 53216 Fig 21 1 Bands loaded in QGIS Semi Automatic Classification Plugin E Band name RT_LC81910312015006LGN00_B7 RT_LC81910312015006LGN00_B5 RT 1 2101N219N15NN
121. he following image Project Edt View Layer Settings Plugins Vector Raster Database Web SCP Processing Help 0 786 0 960 wy sho A ALS IPAE 2 WHS UII 0010000 0 eo 9 100 3 Mo MC info A E I Cito o 1 1 335 336 v o0 Sa Chapter 1 Installation in Windows 32 bit Semi Automatic Classification Plugin Documentation Release 4 8 0 1 1 4 Known issues QGIS 32bit installation could include an old version of NumPy as default in order to use some SCP tools e g Land cover change page 120 the update of NumPy is required Please follow the instructions described in Error 26 The version of Numpy is outdated Why page 208 1 4 Known issues Semi Automatic Classification Plugin Documentation Release 4 8 0 1 10 Chapter 1 Installation in Windows 32 bit CHAPTER 2 Installation in Windows 64 bit 2 1 QGIS download and installation Download the latest QGIS version 64 bit from here the direct download of QGIS 2 8 from this link Execute the QGIS installer with administrative rights accepting the default configuration Now QGIS 2 is installed alata Project Ede wew Layer Settings Plugns Vector Raster Database Web Processing Help DOBAIA CM Z eeLcGqV i ANB SE Hoe 2s JID DP Sasa a A B TBI ML de b HE b o Sio layer D y E Qi i Lo ES ev m en nj 2 2 Semi Automatic Classification Plugin installation Run QGIS 2
122. i romolstor blogspot com Yee er Ww 25 rating vote s 38811 downloads Tags raster landsat spectral signature classification land cover accuracy scatter plot supervised classification dos clip remote sensing mask analysis land cover change More tracker code repository Author Luca Congedo The SCP should be automatically activated however be sure that the Semi Automatic Classification Plu gin is checked in the menu Installed the restart of QGIS could be necessary to complete the SCP installation Semi Automatic Classification Plugin Plugin for the semi automatic supervised classification designed to expedite the processing of multispectral or hyperspectral remote sensing images which provides a set of tools for pre processing and post processing Written by Luca Congedo the Semi Automatic Classification Plugin SCP allows for the semi automatic supervised dassification of remote sensing mages providing tools to expedite the creation of ROIs training cre l Sp tures es EE p semi 0S MEM at asai Classifica o da Cobertura do Solo Clasificaci n d Cobertura de la Tierra Classification de la Couverture du Sol knaccndua wn SeNinenoneaosaWan Klassifizierung der Landbedeckung Classificazione della Copertura del Suolo For more information please visit http iromaystors blogspot com Yero vtr 25 rating vote s 38811 downloads 5 3 Configuration of the plugin Now the Semi Automatic Classif
123. ic Classification Plugin Documentation Release 4 8 0 1 in Variable name for expressions tab Reclassification page 131 following Python operators e g raster gt 3 select all pixels having value gt 3 raster gt 5 raster lt 2 select all pixels having value gt 5 or lt 2 raster gt 2 raster lt 5 select all pixel values between 2 and 5 New value set the new value for the old values defined in Old value Add value add a row to the table Remove highlighted values remove highlighted rows from the table Reclassify choose the output destination and start the calculation reclassified raster is loaded in QGIS Apply symbology from Signature list if checked color and class information are defined from Signature list page 95 according to the selection between MC ID and C ID in the combobox 17 4 Band calc E QJ Semi Automatic Classification Plugin EP Tools af Preprocessing db Post processing i Band calc gm Bandset Settings P About Band list GULES Output raster O Fig 17 16 Band calc tab The Band calc allows for the raster calculation for bands i e calculation of pixel values using NumPy functions Raster bands must be already loaded in QGIS Input rasters must be in the same projection The following video shows this tool http www youtube com watch v vjKX00 ML64 Alternative video link https archive org details video_band_calc 17 4 Band c
124. ic of Several Landsat images page 149 Plugin installation page 149 Download and Pre processing of Landsat images page 150 Classification of Landsat Images page 155 Enhancement of Classification Using NDVI page 167 Cloud Masking page 170 Mosaic of Classifications page 173 Accuracy Assessment page 173 Clip of the Classification page 174 Classification Report page 175 Tutorial Using the tool Band calc page 179 Application of a mask to multiple bands page 179 NDVI Calculation page 181 Classification refinement basing on NDVI values page 183 Other Tutorials page 187 147 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 148 CHAPTER 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images This tutorial is about the land cover classification of several Landsat images in order to create a classification of a large study area using the Semi Automatic Classification Plugin SCP For very basic tutorials see Tutorial 1 Your First Land Cover Classification page 57 and Tutorial 2 Land Cover Classification of Landsat Images page 65 The study area of this tutorial is Costa Rica a Country in Central America that has an extension of about 51 000 square kilometres In particular we are going to classify Landsat 8 and Landsat 7 images masking clouds and creating a mosaic of classifications We are going t
125. ication Plugin is installed and two docks and a toolbar should be added to QGIS Also a SCP menu is available in the Menu Bar of QGIS It is possible to move the Toolbar page 87 and the docks according to your needs as in the following image Project Edt View Layer Settings Plugins Vector Raster Database Web SCP Processing Hep wy sho A ALS IPAE 2 WHS Add to signature b l J amp 0 010000 Of 60 S 100 6 0 786 0 960 The configuration of available RAM is recommended in order to reduce the processing time From the SCP menu 24 Chapter 5 Installation in Mac OS Semi Automatic Classification Plugin Documentation Release 4 8 0 1 page 85 select Al Settings gt Processing nmm In the Settings page 129 setthe Available RAM MB to a value that should be half of the system RAM For instance if your system has 2GB of RAM set the value to 1024MB I D 5 3 Configuration of the plugin 25 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 26 Chapter 5 Installation in Mac OS Part II Brief Introduction to Remote Sensing 27 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Basic Definitions page 31 GIS definition page 31 Remote Sensing definition page 31 Sensors page 33 Radiance and Reflectance page 33 Spectral Signature page 33 Landsat Satel
126. ices AWS Before the use of this tool the download of the Landsat image dabatase about 500MB is required which consist of the following files updated daily http landsat pds s3 amazonaws com scene_list gz http landsat usgs gov metadata_service bulk_metadata_files LANDSAT_8 csv gz http landsat usgs gov metadata_service bulk_metadata_files LANDSAT_ETM csv gz http landsat usgs gov metadata_service bulk_metadata_files LANDSAT_ETM_SLC_OFF csv gz http landsat usgs gov metadata_service bulk_metadata_files LANDSAT_TM 1980 1989 csv gz http landsat usgs gov metadata_service bulk_metadata_files LANDSAT_TM 1990 1999 csv gz http landsat usgs gov metadata_service bulk_metadata_files LANDSAT_TM 2000 2009 csv gz http landsat usgs gov metadata_service bulk_metadata_files LANDSAT_TM 2010 2012 csv gz Images from the Amazon Web Services allows for the download of single bands The following video shows this tool http www youtube com watch v sIGRZOBHWSI Database e Update database update Landsat database only the databases of the satellites checked in Satellites under the tool Search page 110 are downloaded only Landsat 8 if checked only the Landsat 8 database is downloaded from the Amazon Web Ser vices Select database directory Q select a directory where the Landsat database is stored it is recommended to select a custom directory in order to prevent the database deletion when upgrading SCP 108 Chap
127. ifica o da Cobertura do Solo Clasificaci n de la Cobertura de la Tierra Classification de la Couverture du Sol knacenbuxauns 2eMnenons308aHHa Klassifizierung der Landbedeckung Classificazione della Copertura del Suolo For more information please visit http fromgistors blogspot com dede ee eee 25 rating vote s 38811 downloads Tags raster landsat spectral signature classification land cover accuracy scatter plot supervised classification dos Lcip remote sensing mask analysis land cover change Moreinfo homepage tracker code repository Author Luca Congedo The SCP should be automatically activated however be sure that the Semi Automatic Classification Plu gin is checked in the menu Installed the restart of QGIS could be necessary to complete the SCP installation id HIB gt Plugin installed successfully Remove empty layers from the wi Riveras Road graph plugin Rss menu RT MapServer Exporter RT Omero RT QSpider RuGeocoder Sample Rasters Semi Automatic Classification Plugin Plugin for the semi automatic supervised classification designed to expedite the processing of multispectral or hyperspectral remote sensing images which provides a set of tools for pre processing and post processing Written by Luca Congedo the Semi Automatic Classification Plugin SCP allows for the semi automatic supervised classification of remote sensing images providing tools to exped
128. ification Plugin Classification Input J CI Fig 17 13 Classification report 122 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Semi Automatic Classification Plugin Classification Input Fig 17 14 Classification to vector 17 3 Post processing 123 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 17 3 5 Reclassification rs Semi Automatic Classification Plugin we Q9 A J E Fig 17 15 Reclassification The tab Reclassification allows for the reclassification i e assigning a new class code to classification pixels In particular it is useful for converting C ID to MC ID values Classification input Select the classification select a classification raster already loaded in QGIS Refresh list refresh layer list Calculate unique values calculate unique values in the classification and fill the reclassifica tion table automatic C ID to MC ID values using codes from Signature list if checked the reclassification table is filled according to the Signature list page 95 when Calculate unique values is clicked Table fields Old value set the expression defining old values to be reclassified Old value can bea value or an expressions defined using the variable name raster custom names can be defined 124 Chapter 17 Main Interface Window Semi Automat
129. ification Plugin is checked 201 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 202 Chapter 24 Plugin installation CHAPTER 25 Pre processing 25 1 Which image bands should I use for a semi automatic classi fication In general it is preferable to avoid thermal infrared bands If you are using Landsat 4 5 or 7 you should select bands 1 2 3 4 5 7 avoiding band 6 that is thermal infrared for Landsat 8 you should select bands 2 3 4 5 6 7 25 2 Which Landsat bands can be converted to reflectance by the SCP All Landsat 1 2 and 3 MSS and Landsat 4 5 7 and 8 images downloaded from http earthexplorer usgs gov and processed with the Level 1 Product Generation System LPGS can be converted to reflectance automatically by the SCP products generated by the LPGS have a MTL file included that is required for the conversion Since version 3 1 1 the SCP can also convert images from the Global Land Cover Facility images available for free from ftp ftp glcf umd edu glcf Landsat In particular images having an old format of the MTL file or a met file can be processed through the automatic conversion to reflectance and the DOS correction However some images do not have the required information and cannot be processed Also notice that some images available from the Global Land Cover Facility are already converted to reflectance For this process image bands must be renamed in order to
130. image clipping Landsat conversion to reflectance the classification process Minimum Distance Maximum Likelihood Spectral Angle Mapping algorithms and classification previews Be search format ensc ons Pug and the post processing phase conversion to vector accuracy assessment land cover change classification DE soccer report This plugin requires the installation of GDAL OGR Numpy SciPy and Matplotlib Also a virtual machine is available http fromgistors blogspot com p semi automatic os html Keywords 338528 mE Snan 5125 EEE VERITAS o Ml alar os Classifica o da Cobertura do Solo Clasificaci n de la selenext Cobertura de la Tierra Classification de la Couverture du Sol knaccdwkauiws 3emnenone3osanma Klassifizierung der Landbedeckung Classificazione della Copertura del Suolo For more information please X EU Semi Automatic Cassi caton P vice pers fromgistors blogspot com di senaace A De SENSUM Earth Observation Tools Tr fr r 25 rating vote s 38811 downloads 56 Diagram Downloader Category Raster E Spetter Tags Raster Classification Land Cover Remote Sensing Analysis Landsat Land Cover Change Accuracy El Shee Sunenased classification Snertral sianahire Mack Scatter plot Clin DOSI di sera rage Ert Umetpge Reale Es Lu 4 3 Configuration of the plugin Now the Semi Automatic Classification Plugin is installed and two docks and a toolbar should be added to QGIS Also a SCP men
131. in the map it is useful for detecting vegetation pixels characterized by high NDVI values 156 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Fig 20 8 Example of Built up ROI 20 3 Classification of Landsat Images 157 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Fig 20 9 Example of Vegetation ROI 158 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Fig 20 10 Example of Soil ROI 20 3 Classification of Landsat Images 159 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Fig 20 11 Example of Water ROI 160 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 TIP change frequently the Color Composite page 35 and use the buttons jr and X in the Toolbar page 87 for stretching the minimum and maximum values of the displayed image also use the button Show for hiding and showing the image ROIs are used for the calculation of spectral signatures that are used by the classification algorithm in order to classify the entire image In this tutorial we are going to use the Maximum Likelihood page 41 algorithm After the creation
132. ing page 35 The following video shows this tool http www youtube com watch t 4308 v ImbY hilgllg Alternative video link https archive org details video_basic_tutorial_2 start 430 Landsat conversion to TOA reflectance and brightness temperature e Select directory select the Directory containing Landsat bands names of Land sat bands must end with the respective number if the metafile a txt or met file whit the suffix MTL is inside this directory then Metadata page 115 are filled Select directory optional Select MTL file if the metafile a txt or met file whit the suffix MTL is in a directory different than the Directory containing Landsat bands Brightness temperature in Celsius if checked convert brightness temperature to Celsius if a Landsat thermal band is listed in Metadata page 115 if unchecked temperature is in Kelvin e Apply DOS1 atmospheric correction if checked the DOSI Correction page 48 is applied to all the bands thermal bands excluded Use NoData value image has black border if checked pixels having NoData value are not counted during the DOS1 calculation of DNmin it is useful when Landsat image has a black border usually pixel value 0 Perform pan sharpening if checked a Brovey Transform is applied for the Pan sharpening page 35 of Landsat bands Metadata All the bands found in the Directory containing Landsat bands are listed in the metadata table
133. installed o QGIS 006060 Project Edit View Layer Settings Plugins Vector Raster Help DmBEgE xaxqe 3Z 27DP o3 ianmme RX B Bs Paama y TEE i Layers Bs 5 Coordinate 1 193 0 172 Scale 1 453 774 y Y Render EPSG 4326 O Al 3 2 Semi Automatic Classification Plugin installation Run QGIS 2 From the main menu select Plugins Manage and Install Plugins 15 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Project Edit View Layer Settings Plugins _ Vector Raster Help 7 da Manage and Install Plugins TELE pBRDE D E ae Python Console P m aa mm p gt 2 4 e From the menu A11 select the Semi Automatic Classification Plugin and click the button Install plugin B ene Semi Automatic Classification Plugin t ees Plugin for the semi automatic supervised classification designed to expedite the processing of multispectral or hyperspectral remote sensing images which provides a set of tools for pre processing and post processing Upgradeabie Settings Written by Luca Congedo the Semi Automatic Classification Plugin SCP allows for the semi automatic supervised dassification of remote sensing images providing tools to expedite the creation of ROIs training areas through region growing or multiple ROI creation The spectral signatures of training areas can be automatically calculated and dis
134. int MC Info P ROI Macroclass information text C ID P ROI Class ID int C Info P ROI Class information text e Save ROI save the temporary ROI to the Training shapefile page 89 e Add sig list P if checked the spectral signature is calculated the ROI mean value and standard deviation for each raster band and the covariance matrix while the ROI is saved to shapefile it takes some time depending on the number of Input image bands Undo delete the last saved ROI from the Training shapefile page 89 15 5 ROI Signature definition 93 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 94 Chapter 15 ROI Creation dock CHAPTER 16 Classification dock The dock Classification is designed to manage the spectral signatures and classify the Input image Spectral signatures define the characteristics of land cover classes Only spectral signatures in the Signature list page 95 are used by classification algorithms Spectral signatures are calculated from the ROIs of a Training shapefile defined in the ROI Creation dock page 89 In addition spectral signatures can be imported from files from ASTER spectral libraries or from the USGS Spectral Library page 104 Spectral signatures are saved in signature list file xml The use of the Macroclass ID or Class ID for classifications is defined with the option Use Macroclass ID in the Classification algorithm
135. iques that can improve the results which are described in Other Tutorials page 81 11 9 Create the Classification Output 79 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Fig 11 25 Resulting classification 80 Chapter 11 Tutorial 2 Land Cover Classification of Landsat Images CHAPTER 12 Other Tutorials Other Thematic Tutorials page 147 are available about SCP functions Also visit the blog From GIS to Remote Sensing for other tutorials such as Supervised Classification of Hyperspectral Data Monitoring Deforestation Flood Monitoring Estimation of Land Surface Temperature with Landsat Thermal Infrared Band Land Cover Classification of Cropland For other unofficial tutorials also in languages other than English see Where can I find more tutorials about SCP also in languages other than English page 211 81 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 82 Chapter 12 Other Tutorials Part IV The Interface of SCP CHAPTER 13 SCP menu Fig 13 1 SCP menu The SCP menu allows for the selection of the main functions of the Main Interface Window page 101 the Spectral Signature Plot page 135 and the Scatter Plot page 141 E show the Main Interface Window page 101 and display the ROI Creation dock page 89 and the Classification dock page 95 e open the Band set page 127 bX open th
136. irectory LC80150532014050LGN00 of the downloaded Landsat image and the classification 1 NDVI Copy the following Expression page 126 in the Band calc page 125 np where LC80150532014050LGNO0_BOA 53248 LC80150532014050LGN00 BQA 36 Click the button Calculate and select where to save the new classification e g classification 1 clouds tif Clouds are almost completely masked i e Unclassified however some pixels are still classified as Built up in red We can do the same for the image LC80160532014057LGNO00 using the following Expression page 126 in the Band calc page 125 np where LC80160532014057LGNO0 BQA 53248 LC80160532014057LGN00 BOA 36 The Landsat 7 image does not have the QA band Another method for masking clouds uses the Blue and the Thermal Infrared converted to temperature bands basing on the fact that clouds are generally colder than soil 170 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images 64 64 LC80 LC80 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Project Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help c BRAF ami Js AA ERES Ve ue x SCP ROI creation ox oloa taag E e OT TF Classification 1 clouds tif 9 New sho 0 Unclassified CA a 0 une assifie MC ID MC Info 17 info A 4 8 M n 1 r1
137. ite the creation of ROIs training signatures from external sources Also a tool allows for the selection and download of spectral signatures rom the USGS Spectral Library http speclab cr usgs gov spectral lib html Several tools are available for je pre processing phate image clipping Landsat conversion to reflectance the classification process Minimum Distance Maximum Likelihood Spectral Angle Mapping algorithms and classification previews Search A format EPSG CRS Plugin El and the E processing phase conversion to vector accuracy assessment land cover change classification report This plugin requires the installation of GDAL OGR Numpy SciPy and Matplotlib Also a virtual machine is avaiable http fromgistors blogspot com p semi automatic os html Keywords 1320 418 HE AAA NETEN Li eM plac usua Classifica o da Cobertura do Solo Clasificaci n de la Cobertura de la Terra ciasuifcaon de la Couverture du Sol Knaccndwkauins sewnenonbaoBarvin Klassifizierung der Landbedeckung Classificazione della Copertura del Suolo For more information please visit http fromgistors blogspot com Yero vtr 25 rating vote s 38811 downloads 1 3 Configuration of the plugin Now the Semi Automatic Classification Plugin is installed and two docks and a toolbar should be added to QGIS Also a SCP menu is available in the Menu Bar of QGIS It is possible to move the Toolbar page 87 and the docks according to your needs as in t
138. lar energy is NASA 2013 Transmitted The energy passes through with a change in velocity as determined by the index of refraction for the two media in question Absorbed The energy is given up to the object through electron or molecular reactions Reflected The energy is returned unchanged with the angle of incidence equal to the angle of reflec tion Reflectance is the ratio of reflected energy to that incident on a body The wavelength reflected not absorbed determines the color of an object Scattered The direction of energy propagation is randomly changed Rayleigh and Mie scatter are the two most important types of scatter in the atmosphere Emitted Actually the energy is first absorbed then re emitted usually at longer wavelengths The object heats up 31 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 N 6 gt e v o wo vu 2 z c D L Gamma rays 0 1 AA Infra red Thermal IR 100 pm 700 nm 1000 um 1mm Microwaves 1cm 500 MHz 50 MHz Long waves Fig 6 1 Electromagnetic Spectrum i Vi at Y J 32 by Vietor Blhecus SYG version of File Elect romagnetic Speet rum pas CC BY SA 3 0 http creativecommons oro licen Gbaplet 6 Basic Definitions via Wikimedia Commons http commons wikimedia org wiki File 3AElectromagnetic Spectrum svg Semi Automatic Classification Plugin Documentation Rel
139. les of these classes identified in the map 10 6 Create a Classification Preview It is useful to create a Classification preview page 98 in order to assess the results before the final classification Set the colors of the spectral signatures which will represent classes in the classification output in the Signature list page 95 double click the color in the column Color and choose a representative color of each class In the Classification algorithm page 97 select the classification algorithm Spectral Angle Mapping that we are going to use in this tutorial In Classification preview page 98 set Size 500 click the button and then left click the image in the map in order to create a classification preview The result is a square in the map which represent the classification output Previews are temporary classifications and are useful for assessing the effects of spectral signatures during the ROI collection Previews are placed in a group named Class temp group in the QGIS panel Layers In general it is good to perform a classification preview every time a ROI or a spectral signature is added to the list Therefore the phases Create the ROIs page 58 and Create a Classification Preview page 61 should be iterative and concurrent processes 10 7 Create the Classification Output Assuming that the results of classification previews were good i e classes were identified correctly it is possible to perform the actual land
140. lite page 34 Sentinel 2 Satellite page 34 Color Composite page 35 Pan sharpening page 35 Supervised Classification Definitions page 39 Land Cover page 39 Supervised Classification page 39 Training Areas page 39 Classes and Macroclasses page 39 Classification Algorithms page 40 Spectral Distance page 42 Classification Result page 44 Accuracy Assessment page 44 Landsat image conversion to reflectance and DOSI atmospheric correction page 47 Radiance at the Sensor s Aperture page 47 Top Of Atmosphere TOA Reflectance page 47 Surface Reflectance page 48 DOSI Correction page 48 Conversion to At Satellite Brightness Temperature page 51 29 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 30 CHAPTER 6 Basic Definitions This chapter provides basic definitions about GIS and remote sensing 6 1 GIS definition There are several definitions of GIS Geographic Information Systems which is not simply a program In general GIS are systems that allow for the use of geographic information data have spatial coordinates In particular GIS allow for the view query calculation and analysis of spatial data which are mainly distinguished in raster or vector data structures Vector is made of objects that can be points lines or polygons and each object can have one ore more attribute values a raster is a g
141. lsius Also leave checked Use NoData value image has black border TIP check Perform pan sharpening in order to perform the Pan sharpening page 35 of Landsat images producing bands with 15m spatial resolution of course using pan sharpened images increases the classification time because a greater number of pixels need to be processed and can increase the spectral variability Now open the tab Download Landsat page 108 and uncheck the options only if preview in Layers and Load bands in QGIS leave checked Pre process images in order to convert bands to reflectance automatically Click the button Download images from list to select an output directory and start the download process this may take a while When the download process is finished several directories are created in the output directory with the name like Landsat ID containing the original Landsat bands and the converted bands with the suffix converted 152 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Semi Automatic Classification Plugin Yow aoe oe cm Band set SS RT Xt vete O CD D O CD D Search Se foo S FLGNOO LE70150532014090EDC00 oordinate s 1c8o160532014057LGN00 20140226 287 16 53 o iamsesmwssuaum 20160218 2127 0 0357099 S emeve images omit D LE70150532014090EDCO00 2014 03 31 17 24 Downlo
142. ltup 1 Buit 7 1 Buit up 1 Buitupl e 2 v 2 Veget 2 Tees 2 2 Vegetation 2 Tees E 3 v3 Soil 26 Soil7 33 26 Soil7 P ee 4 v4 Water 31 Water7 44 Water 31 Water7 gt lla SS d Jia L export Import Add to signature bL R F Vie Select classification algorithm Threshold Range radius MinROisze MaxROlwidth d iff Maximum Likelihood v 0 0000 J di 0 006000 1 60 100 E Jf _ Use Macroclass ID Y Rapid ROI on band 36 g hi n pr _ Automatic refresh ROI Automatic plot a 500 Redo 9 gt i emu ur 2 e Show Transparency Redo s Show d e Ez ENSE issification style Y Display cursor for NOVI v m Ca Select qml Reset Pa assification out _ Apply mask hs 3 1 9 Macroclass 1 UJ create vector Classification report s Ap eo Cimo gt g Perform classification m oW 1 6 class 1 SCP Classification Layers f i Save ROI v Add sig list cundo a Coordinate 824235 1081696 Scale 1 114 579 v Rotation 0 0 v Render EPSG 32616 Fig 20 14 Classification preview 162 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 nee Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help jm B Em VE ZEN v o EET Es mL Saa Le X GO jd fie i ie h v SCP Classification SCP ROI creation A iw lt lt band set gt gt
143. menu page 85 or the Toolbar page 87 Click the button Select All thenAdd rasters to set andthen Sort by name for ordering bands automatically Finally select Landsat 8 OLI from the combo box Quick wavelength settings in order to set automatically the center wavelength of each band this is required for the spectral signature calcula tion TIP click the button Build band overviews in order to improve display performance of bands E w Semi Automatic Classification Plugin yo Q9 EF Tools of Preprocessing gt Postprocessing gt i Band calc mm Band set gt Settings 7 About Select raster bands RT_LC80150532014050LGN00_B7 Refreshlist RT_LC80150532014050LGN00_B6 o Select all RT_LC80150532014050LGN00_B5 RT_LC80150532014050LGN00_B4 RT_LC80150532014050LGN00_B3 RT_LC80150532014050LGN00_B2 Band set definition RT_LC80150532014050LGN00_B2 RT_LC80150532014050LGN00_B3 3 RT_LC80150532014050LGN00_B4 RT_LC80150532014050LGN00_B5 3 RT_LC80150532014050LGN00_B6 RT_LC80150532014050LGN00_B7 Fig 20 6 Definition of Band set 20 3 Classification of Landsat Images 155 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 In the list RGB select the item 4 3 2 for displaying a Color Composite page 35 of Near Infrared Red and Green A temporary virtual raster of the Band set will be created in QGIS allowing for the photo interpretation of the image Now we ne
144. mp Markham B 2003 Revised Landsat 5 TM radiometric calibration procedures and postcali bration dynamic ranges Geoscience and Remote Sensing IEEE Transactions on 41 2674 2677 e NASA Ed 2011 Landsat 7 Science Data Users Handbook Landsat Project Science Office at NASA s Goddard Space Flight Center in Greenbelt 186 http landsathandbook gsfc nasa gov pdfs Landsat7_Handbook pdf 51 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 52 Chapter 9 Conversion to At Satellite Brightness Temperature Part III Basic Tutorials Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Following the list of basic tutorials that guide you through the steps of a land cover classification Tutorial 1 Your First Land Cover Classification page 57 Data page 57 Load Data page 57 Set the Input Image in SCP page 58 Create the Training Shapefile and Signature List File page 58 Create the ROIs page 58 Create a Classification Preview page 61 Create the Classification Output page 61 Tutorial 2 Land Cover Classification of Landsat Images page 65 Data Download page 65 Automatic Conversion to Surface Reflectance page 66 Clip Data page 69 Create the Band Set page 71 Open the Training Shapefile and Signature List File page 71 Create the ROIs page 73 Create a Classification Preview page 78 Assess Spectral
145. mpled to 30 A vast archive of images is freely available from the U S Geological Survey For more information about how to freely download Landsat images read this 6 7 Sentinel 2 Satellite Sentinel 2 is a multispectral satellite developed by the European Space Agency ESA in the frame of Copernicus land monitoring services Sentinel 2 acquires 13 spectral bands with the spatial resolution of 10m 20m and 60m depending on the band as illustrated in the following table ESA 2015 34 Chapter 6 Basic Definitions Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Sentinel 2 Bands Central Wavelength micrometers Resolution meters Band 1 Coastal aerosol 0 443 60 Band 2 Blue 0 490 10 Band 3 Green 0 560 10 Band 4 Red 0 665 10 Band 5 Vegetation Red Edge 0 705 20 Band 6 Vegetation Red Edge 0 740 20 Band 7 Vegetation Red Edge 0 783 20 Band 8 NIR 0 842 10 Band 8A Vegetation Red Edge 0 865 20 Band 9 Water vapour 0 945 60 Band 10 SWIR Cirrus 1 375 60 Band 11 SWIR 1 610 20 Band 12 SWIR 2 190 20 Sentinel 2 images are freely available from the ESA website https scihub esa int dhus 6 8 Color Composite Often a combination is created of three individual monochrome images in which each is assigned a given color this is defined color composite and is useful for photo interpretation NASA 2013 Color composites are
146. n Documentation Release 4 8 0 1 ROI parameters Range radius Min ROI size Max Rt ROI width 0 010000 4 605M 100 4 __ Rapid ROI on band 14 __ Automatic refresh ROI Automatic plot Fig 15 4 ROI parameters have at least the size defined Min ROI size ifRapid ROI on band is unchecked then ROI could have a size smaller than Min ROI size Max ROI width P set the maximum width of a ROI i e the side length of a square centred at the seed pixel which inscribes the ROI in pixel unit Rapid ROI on band P if checked ROI is created using only the selected band ofthe Input image defined in the combo box the process is quicker if unchecked ROI is the result of the intersection between ROIs calculated using every band the process is longer but ROI is spectrally homogeneous on every band Automatic refresh ROI create automatically a new ROI while Range radius Min ROI size orMax ROI width are changed Automatic plot calculate automatically the ROI spectral signature and display it in the Spectral Signature Plot page 135 spectral signature has MC Info tempo_ROT 15 4 ROI creation ROI creation J use Show PP Y Display cursor for NDVI w It Fig 15 5 ROI creation ROI creation is used for creating ROI polygons these ROIs are temporary until they are saved in the Training shapefile page 89 recall the pointer for ROI creation using the region growing algorithm left
147. n Landsat images page 110 only if preview in Layers if checked the download is performed only for the images listed in Landsat images page 110 that are also displayed as previews in the map Pre process images if checked bands are converted to reflectance and temperature after the download according to the settings defined in Landsat page 114 Load bands in QGIS if checked bands are loaded in QGIS after the download 17 1 Tools 111 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 17 1 6 Download Sentinel F o Semi Automatic Classification Plugin Y y Y EF Tools AL Pre processing Post processing zij Band calc mm Bandset Settings Di lt gt ed Algorithm band weight F Signature threshold ho Download Landsat n Download Sentinel lt gt Login Sentinels https scihub esa inv dhus i User Password Y remember UL X Lon Y Lat LR X Lon Y Lat i Acqusition date from 2014 01 01 v lto 2015 10 08 v Image ID Find images Sentinel images ImageName AcquisitionDate CloudCover Path Display image preview Remove images from list Export links Download images from list Y only if preview in Layers Y Load bands in QGIS Show docks GJ Quick user guide 73 Online help Q Close Fig 17 7 Download Sentinel Thetab Download Sentinel allows for searching and downloading the free Sentinel 2 images from
148. n mosaic is shown in the following image Project Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help gt B ERE as EZ o ERO 059A A b EB X02 O d dle ll ge 8 q pe Layers ox V5 Paga W Y y Bf dassification mosaic E g 0 Unclassified a o A I ai En e 4 fe 9 2 Viv zx ur SCP Classification Layers gt 1 legend entries removed 5 Coordinate 846748 991357 Scale 1 668 389 v Rotation 0 0 y Render EPSG 32616 Fig 20 31 Classification mosaic In the following steps we are going to perform the accuracy assessment and the estimation of land cover area 20 7 Accuracy Assessment Accuracy Assessment page 44 is an important step of a land cover classification In this tutorial we are going to use the Training shapefile as reference for assessing classification accuracy However there other meth ods that can improve the validation reliability see http fromgistors blogspot com 2014 09 accuracy assessment using random points html In QGIS load the classification mosaic and the Training shapefile used for the image LC80150532014050LGNO0 In SCP open the tab Accuracy page 117 and click the buttons Refresh list Select classification_mosaic as the classification to assess and the Training shapefile as refer ence shapefile Also select MC_ID as Shapefile field Click Calculate error matrix and choose the output destination e g accuracy
149. n report SX Casstcatontovector 3 Recerca Y gt Calculate classification report PixelSum Percentage Area metre 2 1167431 3 03643497771 1050730307 87 31562435 82 0924590967 28407338030 8 5513990 14 3416374096 4962791300 12 203567 0 529468516004 183217694 735 Fig 20 37 Classification report 20 9 Classification Report 177 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 178 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images CHAPTER 21 Tutorial Using the tool Band calc This is a tutorial about the use of the tool Band calc page 125 that allows for the raster calculation for bands In particular we are going to calculate the NDVI Normalized Difference Vegetation Index of a Landsat image and then apply a condition in order to refine a land cover classification see Tutorial 2 Land Cover Classification of Landsat Images page 65 basing on NDVI values a sort of Decision Tree Classifier The Band calc page 125 can perform multiple calculations in sequence We are going to apply a mask to every Landsat bands in order to exclude cirrus and cloud pixels from the NDVI calculation and avoid anomalous values In particular Landsat 8 includes a Quality Assessment Band that can be used for masking cirrus and cloud pixels The values that indicate with high confidence cirrus or clouds pixels are for the description of these codes see t
150. nd cover change More info homepage tracker code repository Author Luca Congedo The SCP should be automatically activated however be sure that the Semi Automatic Classification Plu gin is checked in the menu Installed the restart of QGIS could be necessary to complete the SCP installation um Plugin installed successfully o T EJ postales Search Not nstabed PR Remove empty layers from the mi i i inati 1 p vues Semi Automatic Classification Plugin a PY 1 i Hed inch phe Plugin for the semi automatic supervised classification designed to expedite the sex Rss menu processing of multispectral or hyperspectral remote sensing images which provides a BE rr mopserver Exporter set of tools for pre processing and post processing RT Omero Written by Luca Congedo the Semi Automatic Classification Plugin SCP allows for the semi automatic RT QSpider supervised classification of remote sensing images providing tools to expedite the creation of ROIs training RuGeocoder areas through region growing or multiple ROI creation The spectral signatures of training areas can be 5 R automatically calculated and displayed in a spectral signature plot It is possible to import spectral signatures from external sources Also a tool allows for the selection and download of spectral signatures die sese from the USGS Spectral Library http spediab cr usgs gov spectral lib html Several tools are
151. nd to install on it The name you choose will be used throughout VirtualBox to identify this machine a Name Semi Automatic OS Type Linux Version Debian 32 bit Hide Description c Set the memory size the more is the better but this parameter should not exceed a half of he host system RAM for instance if the host system has 1 GB of RAM type 512 MB click Next 193 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Oracle VM VirtualBox Manager File Machine Help af 7 y 1 DI tails Snapshots New Settings Start Disca Create Virtual Machine Memory size al machines on your Select the amount of memory RAM in megabytes to haven t created any virtual be allocated to the virtual machine The recommended memory size is 512 MB 8192 MB d In the Hard drive settings select Use an existing virtual hard drive file and select the downloaded file SemiAutomaticOS vmdk click Create Oracle VM VirtualBox Manager File Machine Help ag 7 y 3 Snapshots Mew Settings Start Discags Create Virtual Machine Hard drive al machines on your If you wish you can add a virtual hard drive to the new Ra cometa buy MUN machine You can either create a new hard drive file or select one from the list or from another location using the folder icon If you need a more complex storage set up you can skip this step and make the changes to the machine settings once the machin
152. nfrared 1 ann AVN Short Wavelength Infrared 2 10 2 Load Data Start QGIS and load sample image tif in QGIS from the QGIS menu Layer Add Raster Layer The image is displayed in QGIS 57 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 jns Vector Raster Database SCP Help Project gr View Layer settings Bug DEBA Pu Ez Ve SPER SACO Mee eee Layer exf ES lt F sample image L E Lu kd 9 Goa p z o mme eo eme 2 a n e p a A e ve range dis Rollos ax NOV with r o am 2 Automatic refresh RO Automatic pot QR Fig 10 1 Image loaded in QGIS 10 3 Set the Input Image in SCP In the SCP 7oolbar page 87 click the button d for refreshing the list Input image Inthe list Input image select sample image In the list RGB select the item 4 3 2 for displaying a Color Composite page 35 of Near Infrared Red and Green The image in QGIS will be updated accordingly Fig 10 2 Color composite RGB 4 3 2 of Input image 10 4 Create the Training Shapefile and Signature List File In order to collect Training Areas page 39 ROIs and calculate the Spectral Signature page 33 thereof we need to create the Training shapefile and Signature list fileinSCP In the ROI Creation dock page 89 click the button New shp and define a name e g ROI shp in order to create the Training shapefile that will store ROI polygons The shapefile is c
153. nt 20 8 Clip of the Classification Before calculating the area of each land cover class we need to clip the classification to the extent of the study area which is Costa Rica Download the Shapefile of Sub National Administrative Units of Costa Rica from http data fao org map entryId c720f990 88fd 1 1da a88f 000d939bc5d8 amp tab metadata clicking the Download button by the FAO Extract the downloaded file 1173 zip and load the shapefile costa rica shp in QGIS select WGS84 as projection Project Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help Je BRAAF mmn aen 8H Ae Oe A xo TS ys Layers ox TUS O 9 e e e M E v Ml costa rica amp g e A e 5a e gt Y b 2 oa Vor c a al SCP Classification Layers S Coordinate 83 651 8 048 Scale 11 1 965 625 v Rotation 0 0 y Render Q EPSG 4326 OTF Fig 20 33 The shapefile of Costa Rica by FAO 174 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 In this case we need to define the projection of this shapefile In QGIS open the command Vector gt Data management tool gt Define current projection select the shapefile costa rica as Input vector layer and choose EPSG 4326 WGS 84 as spatial reference and click OK g aie Define current projection Input vector layer costa rica Input
154. o identify the following land cover classes 1 Built up 2 Vegetation 3 Soil 4 Water Following the video of this tutorial http www youtube com watch v acxmIrM Qns Alternative video link https archive org details video_tutorial_Landsat_mosaic_ENG 20 1 Plugin installation First install the SCP For information about the installation of QGIS in various systems see Plugin Installation page 5 Open QGIS from the main menu select Plugins gt Manage and Install Plugins Project Edit View Layer Settings Plugins Vector Raster Database Processing Help O amp l h N NW k 1 Manage and Install Plugins p eroii BU 3 E E mv O O E M MD 4 Python Console ES y EJ x E bes isi From the menu A11 select the Semi Automatic Classification Plugin and click the button Install plugin The SCP should be automatically activated however be sure that the Semi Automatic Classification Plugin is checked in the menu Installed the restart of QGIS could be necessary to complete the SCP installation We are going to set some of the SCP options in order to optimize the following processes Open the Settings page 129 clicking the button E and select Settings Processing page 131 Now in Classification process page 131 check the options Use virtual rasters for temp filesandRaster compression in order to save disk space during the processing 149 Semi Automatic Classification Plugin Documenta
155. od you should be familiar with GitHub This translation method allows for the translation of the PO files locally 1 Download the translation files Go to the GitHub project https github com semiautomaticgit SemiAutomaticClassificationManual_v4 tree master local and download the po files of your language you can add your language if it is not listed or you can fork the repository Every file po is a text file that refers to a section of the User Manual 2 Edit the translation files Now you can edit the po files It is convenient to edit those file using one of the following programs for instance Poedit for Windows and Mac OS X or Gtranslator for Linux or OmegaT Java based for Windows Linux and Mac OS X These editors allow for an easy translation of every sentence in the User Manual 212 Chapter 28 Other
156. on page 92 74 Chapter 11 Tutorial 2 Land Cover Classification of Landsat Images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Project Edit View Layer Settings Plugins Vector Raster Database SCP Help vs Gs eb E A DEBRAJ Pm e wi ox vv ROI IF clip Rr Lc81910312015006LGNO00 IF clip Rr Lc81910312015006LGN00 Wf cip Rr 1c81910312015006LGN00 v vB A gt FF clip Rr 1C81910312015006L GNOO e 9 Wf clip RT Lc81910312015006LGN00 PP dip Rr 1c81910312015006LGNO00 i fle ML Het gt MC ml Water 1 Lake 22 Built up 2 Buildings 3 3 Vegetation 3 Vegetation 394 Bare soil 4 Bare soil 0010000 SI eo c 1000 1_ Rapid ROI on band 1 Automatic refresh ROI _ Automatic plot r ynature definition 1 O Macroclass 1 1 C Class 1 DpbDOu vwe 45 a eS a N e n ayers AS Lee 311812 4630651 1117682 MR Fig 11 14 Color composite RGB 3 4 6 Project Edit View Mer Rede Plugins Vector Raster Database SCP Help Dg BR 2 PI eee vs BED bs LX CIO cier o As wy SCP ROI creation ox E um mm LAE CI n I Mco Kw Cinto BR dip rr cemono im ve T T clip_KT_L C819103120150061 Gn ter 1 Lake P B LE pee Em M o tea 3 a Bf clip hr Lce1910312015006LGNO0 Bare sol Bare son Aa a gt Wf dip RT_LCB1910312015006LGN00 ie AR P tonnene tn E i asane iE L js a Mis Range radius IF 0 036000 6
157. on C_info string The following video shows this tool http www youtube com watch t 230 amp v nZffzX_sMnk Alternative video link https archive org details video_basic_tutorial_1 start 230 P Configuration stored in the active project of QGIS Q Configuration stored in QGIS registry 15 1 Training shapefile Training shapefile P select a shapefile from a list of shapefiles containing the required fields loaded in QGIS ROIs polygons are saved in this shapefile u refresh layer list New shp create a new shapefile containing the required fields Macroclass ID Macroclass Information Class ID and Class Information 89 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 SCP ROI creation 0 010000 10 1 om ROI creation ROl Signature definition R v Undo Fig 15 1 ROI creation dock Fig 15 2 Training shapefile 90 Chapter 15 ROI Creation dock Semi Automatic Classification Plugin Documentation Release 4 8 0 1 WARNING In order to avoid data loss do not edit the Training shapefile in QGIS while it is loaded in the Training shapefile page 89 15 2 ROI list ROI list E MC ID MC Info CAD C Info Add to signature pa m Fig 15 3 ROI list The ROI list displays the ROI polygons collected in the Training shapefile Double click on any table item to zoom in the map to that ROI also ROIs can be edi
158. on by manually drawing a polygon or with an automatic region growing algorithm TIP Install the OpenLayers Plugin in QGIS and add a map e g OpenStreetMap in order to facilitate the identification of ROIs using high resolution data After clicking the button in the ROI creation page 92 you should notice that the cursor in the map displays a value changing over the image This is due to the function Display cursor for NDVI in the ROI creation page 92 which displays the NDVI value of the pixel beneath the cursor The NDVI value can be useful for identifying pure pixels in fact vegetation has higher NDVI values than soil For instance move the mouse over a vegetation area and left click to create a ROI when you see a local maximum value This way the created ROI and the spectral signature thereof will be particularly representative of healthy vegetation Create several ROIs the more is the better In general you should create one ROI for each color that you can distinguish in the image Therefore change the color composite in order to identify the different types of land cover TIP Change frequently the Color Composite page 35 in order to clearly identify the materials at the ground use the mouse wheel on the list RGB for changing the color composite rapidly A few examples of ROIs are illustrated in the following figures It is worth mentioning that you can show or hide the temporary ROI by switching Show ROI in ROT creati
159. on the season of the images Therefore the input images should be acquired in the same period in order to avoid differences due for instance to the phenological state of vegetation or occurred land cover change 20 9 Classification Report 175 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Clipper Input file raster classification mosaic w Select Output file er Desktop classification clip tif Select Mo data value 0 Clipping mode Extent e Mask layer Mask layer costa rica w Select _ Create an output alpha band Load into canvas when finished gdalwarp dstnodata 0 q cutline 2 PA Ihome user Desktop 11 73 costa rica shp L crop to cutline of GTiff G Imnt Disk Landsat classification mosaic tif qe fhome user Neskton classification clio tif hel E Help Ok Q Close Fig 20 35 Clipping the classification 176 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 2 Vegetation 3 Soil 4 Water oF dassification_mosaic 0 Unclassified 1 Built up 83 387 8 585 1 965 625 v foo Sia Fig 20 36 The clipped classification p QJ Semi Automatic Classification Plugin Y ES EF Tools of Pre processing B Post processing Band calc sax Bandset Settings 7 About FE Accuracy Fi Land cover change Classificatio
160. or every image because NDVI can vary from image to image 20 4 Enhancement of Classification Using NDVI 167 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Project Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help Cm B ERO UE al cm 0o BER 22 on tA b FB AQ ih i V SCP Classification ox SCP ROI creation ox e SERE Reset ROI v 9 Newshp amp ui z S civi MCinfo CID Cinfo Color MCID MCInfo Ev Cinfo A i vio Und 6 Clou 171 Buil up 1 Built upl D 12 Y 1 Built 1 Built JRS Built up 2 Built up2 a v2 Veg 3 Forest 32 Vegetation 3 Vegetation p Ee ja x2 veg 4 Fore 473 soil 4 Soil E Is v 2 veg 16 veg S lo Unclassified 5 Clouds H e 6 v2 Veg 15 Shr 6 4 Water 6 Water2 iD S5 UL t2 Men 28 Cro 2 Vegetation 7 Vegetation2 v le TO lg lbs 1 is I Export Import Addto signature b l i z Class Sa Min ROI size Max ROI width V 4e Maximum Likelihood v 0 0000 gf 0 006000 0 100 x iP Y Use Macroclass ID v Rapid ROI on band 1 cp Classification preview Automatic refresh RO Automatic plot a Jun na mr E Show Transparency m Redos J _ Show O fj j las Y Display cursor for NOV v m 1 Reset o M E adio R ature definition Pa mc MC Info e E Reset 1 9 Built up LU create vector _ Classification report CD em Perform classification 1 i Buil
161. ould be related to a wrong installation Please uninstall QGIS and install it again with administrative rights Delete also the directory qgis2 in your user directory Then run QGIS 2 and try to install the plugin following the Plugin Installation page 5 guide Also it could be related to the user name containing special characters Please try the installation creating a new user without special characters e g user Also if the error message contains something like sfnt4 sfnt4 decode ascii lower it could be related to a known issue of Matplotlib a Python library in order to solve this you should as reported at stackoverflow l open in a text editor the file font managerpy which is inside the directory C PROGRA 1 QGISCH 1 apps Python27 lib site packages matplotlib 2 search for the line sfnt4 sfnt4 decode ascii lower 3 and replace it with the line sfnt4 sfnt4 decode ascii ignore lower Alternatively try to install QGIS through the OSGEO4W installer which includes an updated Matplotlib version 27 4 Error Plugin is damaged Python said ascii Why 209 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 210 Chapter 27 Errors CHAPTER 28 Other 28 1 What are free and valuable resources about remote sensing and GIS The following links are remote sensing and Landsat images The Landsat 8 Data Users Handbook by USGS The Landsat 7 Science
162. owing image we can see that clouds are now masked However pixels near the border of clouds are classified incorrectly as Built up In the next paragraphs more effective methods are described for masking clouds after the classification process see Cloud Masking page 170 When we are happy with the results of the previews we can perform the classification of the whole image In Classification algorithm page 97 activate the checkbox Use Macroclass ID In the Classification output page 99 click the button Perform classification and define the name of the classification output e g 20 3 Classification of Landsat Images 163 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Pest Edit View Layer Settings Plugins Vector Raster Database Web SCP Processing Help DEBRA VE EE lt 2 HER a A de IL 8h 4 lt lt band set gt gt SCP Classification SCP ROI creation ox e E hapefile RO vilu Newshp E CA jj ICI MC Info CID Cinfo Color s MCD MCio Kv C Info LI vio Uncla 24 Clouds MN 11 Built up 1 Built upl 2 21 Built up 1 Built 2 2 Vegetation 2 Tees 3 v 2 Veget 2 Trees 3 0 Unclassified 24 Clouds 4 v3 Soil 26 Soil7 43 Soil 26 Soil7 S w 4 Water 31 Water7 S4 Water 31 Water7 Add to signature bel ik AI RO parameters Range radius Min ROisize Max ROI width MODAS plat s Export Import 7 de Maximum Likelihood w 0 0000 a 0 006000 CM 60 100 CSS Jj U
163. p 15 B amp Q PIO lt lt bona set gt gt vio SE A ACI A 5 O6 B Y Layers w SCP ROI creation dm ar vada PIDA x o B w BF dip RT LC81910312015006LGN00 g B 0 0202136 0 0799775 Bf clip RT Lc81910312015006LGNO00 1024 0 345555 EM v HF clip RT LC81910312015006L GNO0 o W o 0160243 261274 N5 v v BM dip RT LC81910312015006LGNO0 W 0 01 30209 0 010000 60 100 9 0 178342 Rapid ROl on band ex ge Wow Bl study area Frascati Automatic refresh RO Automatic plot A E c2 a PR r 1 Macroclass 1 cio C info 1 class 1 E z TFA Save RO v Add sig list und A Coordinate 312066 4621867 J Scale 1 116 341 v Rotation 0 0 C v Render EPSG 32633 4 Fig 11 12 Band set defined The name of the Training shapefile is displayed in Training shapefile page 89 of the ROI Creation dock page 89 and ROIs are listed in the ROJ list page 91 Also click the button Open in the Classification dock page 95 and select the Signature list file pre viously created e g SIG xml The path of the Signature list file is displayed in Signature list file page 95 and the spectral signatures are loaded in the Signature list page 95 Project Edit View Layer Settings Plugins Vector Raster Database SCP Help SBBA EF E lt lt band set gt gt vs EE em DX E AE BP 2 GIG WL BE bata gt SCP Classification ox SCP ROI crea
164. p multiple rasters page 116 clicking the button Y in the SCP menu page 85 or the Tool bar page 87 Under Raster list click the button Refresh list and the Landsat bands loaded in QGIS will be listed in the table Click the button Select all in order to clip all the images Under Clip coordinates check Use shapefile for clipping and click the button Refresh list in order 11 3 Clip Data 69 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Project Edit View Layer Settings Plugins Vector Raster Database SCP Help CA EP E lt bond set gt gt s E vaL FERARO Akeke BH qpe Layers ox SCP ROI creation EL raria shape O s New shp e MC ID MC info co C Info 9 436 RT_LC81910312015006LGN00_B4 Add to signature a od 0 Range radius Min ROI size Max ROI width v BP RT LC81910312015006LGNO00 B2 oc 0 100 mo oa 0 281 Rapid RO on band 1 Automatic refresh ROI Automatic plot a a T a v Display cursor for NDVI v Show ROI al MC iD MC info 1 Macrocla to C info i 19 lass_1 Layers SCP Classification Y Add sig list un a Coordinate 3788674506923 Scale 1 1 537 828 v Rotation o c S v Render EPSG 32633 Fig 11 7 Converted Landsat bands Project Edit View Layer Settings Plugins Vector Raster Database SCP Help D EB RQA PRA vis EE vak Y ds o e B iun ox SCP ROI creation ox 9 S nvaga Training shape le ae Y B study area
165. page 97 The classification can be performed for the entire image Classification output page 99 or a part of it creating a Classification preview page 98 The following video shows this tool http www youtube com watch t2718 amp v nZffzX sMnk Alternative video link https archive org details video basic tutorial l1 start2718 P Configuration stored in the active project of QGIS Q Configuration stored in QGIS registry 16 1 Signature list file Open P open a signature list file a xml file loading the signatures in the Signature list page 95 and displaying the file path absolute or relative path according to QGIS project settings Save save the signature list to the open file if no signature list is open a window will ask for the creation of a new signature file Reset clear the path of the signature list file 16 2 Signature list The Signature list displays loaded spectral signatures Spectral signatures are automatically saved in the Signature list file page 95 every time the QGIS project is saved or when the button Save is clicked In order to highlight items perform a mouse selection in the table Table fields S checkbox field only the spectral signatures checked in this list are used for the classification process double click on any item to check uncheck all the items in the list MC ID signature Macroclass ID int it can be edited with a single click 95 S
166. pervised classification is an image processing technique that allows for the identification of materials in an image according to their spectral signatures There are several kinds of classification algorithms but the general purpose is to produce a thematic map of the land cover Image processing and GIS spatial analyses require specific software such as the Semi Automatic Classification Plugin for QGIS 7 3 Training Areas Usually supervised classifications require the user to select one or more Regions of Interest ROIs also Training Areas for each land cover class identified in the image ROIs are polygons drawn over homogeneous areas of the image that overlay pixels belonging to the same land cover class 7 4 Classes and Macroclasses Land cover classes are identified with an arbitrary ID code i e Identifier SCP allows for the definition of Macroclass ID i e MC ID and Class ID i e C ID which are the identification codes of land cover classes A Macroclass is a group of ROIs having different Class ID which is useful when one needs to classify materials that have different spectral signatures in the same land cover class For instance one can identify grass e g ID class landMacroclass ID 1 andtrees e g ID class 2andMacroclass ID 1 as vegetation class e g Macroclass ID 1 Multiple Class IDs can be assigned to the same Macroclass ID but the same Class ID cannot be assigned to multiple Macroclass IDs as shown in th
167. played in a spectral signature plot It is possible to import spectral Signatures from external sources Also a tool allows for the selection and download of spectral signatures from the USGS Spectral Library http speciab cr usgs gov spectral lib html Several tools are available for the pre processing phase image clipping Landsat conversion to reflectance the classification process Minimum Distance Maximum Likelihood Spectral Angle Mapping algorithms and classification previews B search amp format EPSG CRS Plugn and the post processing phase conversion to vector accuracy assessment land cover change classification di seeur report This plugin requires the installation of GDAL OGR Numpy SciPy and Matplotlib Also a virtual de machine is available http fromgistors blogspot com p semi automatic os html Keywords MESA du seed 5385 LIOR MTEI uo planta sanas Classifica o da Cobertura do Solo Clasificaci n de la Cobertura de la Tierra Classification de la Couverture du Sol knaccnduxauns zennenonsosanna Klassifizierung der Landbedeckung Classificazione della Copertura del Suolo For more information please visit http fromgistors blogspot com lb SENSUM Earth Observation Tools Yew We Wo 25 rating vote s 38811 downloads Bsc Diagram Downloader DY shapefie Encoding Fier Tags raster landsat spectral signature classification land cover accuracy scatter plot supervised dassification dos1 clip remote sensing mask analysis la
168. porter set of tools for pre processing and post processing RT Omero gt Written by Luca Congedo the Semi Automatic Classification Plugin SCP allows for the semi automatic RT QSpider supervised dassification of remote sensing images providing tools to expedite the creation of ROIs training RuGeocoder areas through region growing or multiple ROI creation The spectral signatures of training n canbe 5 rU automatically calculated and displayed in a spectral signature plot It is possible to import spectr signatures from external sources Also a tool allows for the selection and download of spectral a ST from the USGS Spectral Library http speclab cr usgs gov spectral lib html Several tools are available for Scipttumer the pre processing phase image clipping Landsat conversion to reflectance the classification process Minimum Distance Maximum Likelihood Spectral Angle Mapping algorithms and classification previews Bie search amp formatePsc cRSPugn and the post processing phase conversion to vector accuracy assessment land cover change classification SelectPlusFR report This plugin requires the installation of GDAL OGR Numpy SciPy and Matplotlib Also a virtual machine is available li fromgistors blogspot com p semi automatic os html Keywords 3831 dr SelectTools 325 HIRE DEBES Gol laxat assa Classifica o da Cobertura do Solo Clasificaci n de la selenext Cobertura de la tall
169. probability is less than threshold value max 100 for Spectral Angle Mapping pixels are unclassified if spectral angle distance is greater than threshold value max 90 17 1 Tools 107 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Signature threshold Table fields MC ID signature Macroclass ID MC Info signature Macroclass Information C ID signature Class ID C Info signature Class Information Threshold signature threshold e Threshold value value of threshold used with the button Set threshold value e Set threshold value set the value defined in Threshold value for all the highlighted sig natures in the table e Reset thresholds reset all signatures thresholds to 0 i e no threshold used Automatic thresholds calculate automatically a threshold for all the highlighted signatures based on the standard deviation thereof currently works for Minimum Distance and Spectral Angle Mapping calculating the distance or angle between mean signature and mean standard deviation signature Multiplicative value each threshold value calculated with Automatic thresholds is multiplied by this value 17 1 5 Download Landsat The tab Download Landsat allows for searching and downloading the Landsat Satellite page 34 4 5 7 and 8 images of the whole world from the 80s to present days freely available through the Google Earth Engine and the Amazon Web Serv
170. re list 10 7 Create the Classification Output 63 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 43 ho i Spectral Angle Mapping 00000 Lal Fig 10 11 Classification preview displayed over the image Project Edt View Layer Settings Plugins vector Raster Database SCP Help BBQ GES ove nae vo E ve PERS ADS Mire 5 o gt lla Ut Mais export impor LAdd to signature 1L AL a aan dea 1 oe roto A EE So 0000 C O STADE api no on bar o Automatic refresh ROI Automatic plot lt 2 Show Sza 100 SJL zac m n j sector z E a _ Apply mask e soi Crema vedo Canaan reden cuates i SCP Classification layers B 4959592545 lo An CMA POOL LAGZL 99 RASO AO or E Sl 9 Fig 10 12 Result of the land cover classification Fig 10 13 Example of error Bare soil classified as Built up 64 Chapter 10 Tutorial 1 Your First Land Cover Classification CHAPTER 11 Tutorial 2 Land Cover Classification of Landsat Images This tutorial describes the main phases for the classification of images acquired by Landsat Satellite page 34 In addition some of the SCP tools are illustrated In this tutorial we are going to classify a Landsat 8 image acquired over Frascati Rome Italy in order to identify the following land cover classes 1 Water 2 Built up 3 Veget
171. reate a virtual raster manually in QGIS ssaa 26 Tutorials 26 1 Why using only Landsat 8 band 10 in the estimation of surface temperature 101 102 114 117 125 127 129 135 136 141 142 145 149 149 150 155 167 170 173 173 174 175 179 179 181 183 187 189 193 197 201 201 203 203 203 203 203 204 204 205 205 27 Errors 207 27 1 How can reportan emot o o 25cm o m o8 9o ee eee a RR 207 27 2 Why am I having issues during the creation of the Landsat virtual raster 208 27 3 Error 26 The version of Numpy is outdated Why o o 208 27 4 Error Plugin is damaged Python said ascii Why o o o 209 28 Other 211 28 1 What are free and valuable resources about remote sensing and GIS 211 28 2 Whereican I ask anew QUESTION 3o e to le voce RU SOROR 9m E 211 28 3 Where can I find more tutorials about SCP also in languages other than English 211 28 4 How can I translate this user manual to another language o 212 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Written by Luca Congedo the Semi Automatic Classification Plugin SCP is a free open source plugin for QGIS that allows for the semi automatic classification also supervised classification of remote sensing images Also it provides several tools for the pre processing o
172. reated and added to QGIS The name of the Training shapefile is displayed in Training shapefile page 89 Also click the button Save in the Classification dock page 95 and define a name e g SIG xml in order to create the Signature list file that will store spectral signatures The path of the Signature list file is displayed in Signature list file page 95 10 5 Create the ROIs We are going to create ROIs defining the Classes and Macroclasses page 39 The Macroclass ID codes are illustrated in the following table of course one can define different codes and classes according to the needs 58 Chapter 10 Tutorial 1 Your First Land Cover Classification Semi Automatic Classification Plugin Documentation Release 4 8 0 1 reject Ed View Layer Stings faster D DOmBE X Fame eee A A CO uta gt SCP ROI creation i ROLshp y Lo New shp e D0 y jP 5 n p e aosa i L ug 5 Lo meee ar Range radius Min ROI size Max ROI width 0 010000 eot 100 0 _ Rapid ROI on band T Automatic refresh ROI _ Automatic pt GR ict ceato L8 RU Viene nov vi s Shown ET Fig 10 3 Definition of Training shapefile and Signature list file in SCP Macroclass name Macroclass ID Water 1 Built up 2 Vegetation 3 Bare soil 4 ROIs can be created by manually drawing a polygon or with an automatic region growing algorithm Zoom in the map over the dark area it is a lake in the lower
173. rid or image where each cell has an attribute value Fisher and Unwin 2005 Several GIS applications use raster images that are derived from remote sensing 6 2 Remote Sensing definition A general definition of Remote Sensing is the science and technology by which the characteristics of objects of interest can be identified measured or analyzed the characteristics without direct contact JARS 1993 Usually remote sensing is the measurement of the energy that is emanated from the Earth s surface If the source of the measured energy is the sun then it is called passive remote sensing and the result of this measurement can be a digital image Richards and Jia 2006 If the measured energy is not emitted by the Sun but from the sensor platform then it is defined as active remote sensing such as radar sensors which work in the microwave range Richards and Jia 2006 The electromagnetic spectrum is the system that classifies according to wavelength all energy from short cosmic to long radio that moves harmonically at the constant velocity of light NASA 2013 Passive sensors measure energy from the optical regions of the electromagnetic spectrum visible near infrared i e IR short wave IR and thermal IR see Figure Electromagnetic Spectrum page 32 The interaction between solar energy and materials depends on the wavelength solar energy goes from the Sun to the Earth and then to the sensor Along this path so
174. rsion to At Satellite Brightness Temperature imple mented in SCP Landsat page 114 For information about how to estimate surface temperature read this post For Landsat thermal bands the conversion of DN to At Satellite Brightness Temperature is given by from https landsat usgs gov Landsat8_Using_Product php Tg K2 In K1 L 1 where e K Band specific thermal conversion constant in watts meter squared ster um e K Band specific thermal conversion constant in kelvin and L is the Spectral Radiance at the sensor s aperture measured in watts meter squared ster jm for Landsat images it is given by from https landsat usgs gov Landsat8 Using Product php Ly Mr Qca Ar where e Mr Band specific multiplicative rescaling factor from Landsat metadata RADI ANCE_MULT_BAND_x where x is the band number Ar Band specific additive rescaling factor from Landsat metadata RADIANCE ADD BAND x where x is the band number Q al Quantized and calibrated standard product pixel values DN The K and K constant for Landsat sensors are provided in the following table Constant Landsat 4 Landsat 5 Landsat 7 K watts meter squared ster um 671 62 607 76 666 09 K Kelvin 1284 30 1260 56 1282 71 from Chander amp Markham 2003 from NASA 2011 For Landsat 8 the K and K values are provided in the image metafile References Chander G a
175. s a tif file showing the errors in the map where pixel values represent the categories of comparison i e combinations identified by the ErrorMat rixCode in the error matrix between the classification and reference Also a text file containing the error matrix i e a csv file separated by tab is created with the same name defined for the tif file The following video shows this tool 17 3 Post processing 117 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Semi Automatic Classification Plugin Fig 17 10 Split raster bands 118 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Semi Automatic Classification Plugin Fig 17 11 Accuracy 17 3 Post processing 119 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 http www youtube com watch t 27808v acxmIrM Qns Alternative video link https archive org details video tutorial Landsat mosaic ENG start22780 Error Matrix Input Select the classification to assess select a classification raster e Select the reference shapefile or raster selecta raster or a shapefile used as reference layer ground truth for the accuracy assessment Shapefile field ifa shapefile is selected as reference select a shapefile field containing numeric class values Refresh list refresh layer list Calculate error matrix choose the outp
176. s generally lower than physically based corrections but they are very useful when no atmospheric measurements are available as they can improve the estimation of land surface reflectance The path radiance is given by Sobrino et al 2004 Ly Lmin Lpoi where e Lmin radiance that corresponds to a digital count value for which the sum of all the pixels with digital counts lower or equal to this value is equal to the 0 01 of all the pixels from the image considered Sobrino et al 2004 p 437 therefore the radiance obtained with that digital count value DN nin e Lpoiy radiance of Dark Object assumed to have a reflectance value of 0 01 Therfore for Landsat images Lmin Mr DN min AL The radiance of Dark Object is given by Sobrino et al 2004 Lpo1 0 01 ESUN x cosh Tz Edown To n d Therefore the path radiance is Ly Mz DNmin Az 0 01 x ESUN cos0 T Edown To n d 48 Chapter 8 Landsat image conversion to reflectance and DOS1 atmospheric correction Semi Automatic Classification Plugin Documentation Release 4 8 0 1 There are several DOS techniques e g DOS1 DOS2 DOS3 DOS4 based on different assumption about Ty T and Edown The simplest technique is the DOSI where the following assumptions are made Moran et al 1992 T 21 T 21 Edown 20 Therefore the path radiance is Ly Mr DNmin Az 0 01 ESUN x cos0s n d
177. s useful for assessing ROI separability see Spectral Distance page 42 The following spectral distances are calculated Jeffries Matusita distance range 0 identical 2 different useful in particular for Maximum Likelihood page 41 classifications Spectral angle range 0 identical 90 different useful in particular for Spectra Angle Mapping page 42 classifications Euclidean distance useful in particular for Minimum Distance page 40 classifications Bray Curtis similarity range 0 different 100 identical useful in general Values are displayed in red if signatures are particularly similar 138 Chapter 18 Spectral Signature Plot Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Fig 18 5 Spectral Signature Spectral distances C ID 3 MC info Vegetation C ID 3C info Trees A mc _ 10 2 Mc info Built up C ID 2C info Built upl Jeffries Matusita distance 1 99999960566 Euclidean distance 0 271459177347 Bray Curtis similarity 96 72 9423554866 Fig 18 6 Spectral Signature Example of spectral distances 18 1 Plot Signature list 139 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 140 Chapter 18 Spectral Signature Plot CHAPTER 19 Scatter Plot The Scatter plot window allows for the calculation of the ROI scatter plots which are useful for assessing ROI separability between
178. se Macroclass ID Y Rapid ROI on band 39 e o _ Automatic refresh ROI Automatic plot y Bm 0x09 CER RO creation t3 D s Show Tansparency Redos 3 Show O gt EX i assificat tyle Y Display cursor for NDV v t 5 e Select qmi Reset pz fication output ose M 0 Unclassified U __ Create vector _ Classification report CD Cio Perform classification 24 C M Clouds SCP Classification Save ROI iv Add sig list Undo Coordinate 839727 1101843 Scale 1 126 080 v Rotation 0 0 v Render QEPSG 32616 Fig 20 17 Classification preview over clouds TIP load a service such as OpenStreetMap using the OpenLayers Plugin which can ease the photo interpretation and the ROI creation ops Edt View Layer Settings Plugins Vector Raster Database Web SCP Processing Help amp 5 BRA 1 E ar a bend ant gt gt voc EE ee or A iS Rr WM 15 O d ge e t V Layers SCP ROI creation fu oo ayaan IA Ro v JLo Newshp gt g Y OpenStreetMap COL r v PP band set vrt MCIO MCinfo Iv C info AUT A WM RT_LC80150532014050LGNO0_B7 I1 Built up 1 Bulltupl 4 Mo 2 2 Vegetation 2 Tees 3 0 376693 3 2 Vegetation 3 Grassland S e BF RT 1C80150532014050L GNO0 B6 43 Soil 4 Soill 33 o 53 soil 5 Soil2 0 529091 2 Vegetation 6 Forest a PF RT_LC80150532014050LGN00 BS s coe x p Y o 7 0 Unclassified 7 Clouds v D
179. search for specific Sentinel images using the Image ID or name Acquisition date from to define the range of acquisition dates Image ID search only the Image ID or name of Sentinel images e Find images start searching Sentinel images results are displayed inside the table in Sentinel images page 113 Sentinel images Image list This table displays the results of the Sentinel search Table fields ImageName the Sentinel image name AcquisitionDate date of acquisition of Sentinel image CloudCover percentage of cloud cover in the image not used Path path of the image not used Row row of the image not used min lat minimum latitude of the image min lon minimum longitude of the image max lat maximum latitude of the image max lon maximum longitude of the image Size the size of the image Preview URL of the image preview ImagelD the Sentinel Image ID e Display image preview display image preview of highlighted images in the map preview are roughly georeferenced on the fly e Remove images from list remove highlighted images from the list Clear table remove all images from the list 17 1 Tools 113 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Download It is possible to download multiple images i e all the images in the image list table During the download it is recommended not to interact wi
180. sions 17 5 Band set E Ww Semi Automatic Classification Plugin Y o Q9 EF Tools f Pre processing HP Postprocessing Band calc s Bandset Settings 7 About Band list Band list ee Select raster bands Refresh list Selectall Add rasters to set e as ote Band set definition Fig 17 17 Band set The tab Band set allows for the definition of a set of single band rasters loaded in QGIS used as Input image The Center wavelength of bands can be defined which is required in order to calculate properly the spectral signatures If a band set of single band rasters is defined then the item lt lt band set gt gt will be listed in the Toolbar page 87 as Input image 17 5 Band set 127 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 The Band set is stored in the QGIS project 17 5 1 Band list List of single band rasters loaded in QGIS e Refresh list refresh raster band list e Select all select all raster bands Add rasters to set add selected rasters to the band set 17 5 2 Band set definition Definition of bands composing the Input image Although it is recommended to define the Center wavelength of bands it is possible to assign the band number instead of the wavelength Of course the USGS Spectral Library page 104 will not be useful but the ROI collection and the classification process will still be working It is possible to d
181. spectral libraries directly from internet n open a window for exporting signatures every signature is exported as a csv file in the selected directory Export export the signature list to a new signature file i e a xml file for the Signature list file page 95 y Import import a xml file adding the spectral signatures to the ones already loeaded in the Signature list 16 3 Classification algorithm ef open the A gorithm band weight page 106 for the definition of band weights Select a classification algorithm select one of the Classification Algorithms page 40 available classification algorithms are Minimum Distance page 40 Maximum Likelihood page 41 Spectra Angle Mapping page 42 16 3 Classification algorithm 97 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Classification algonthm Select classification algorithm Threshold de Minimum Distance wil 00000 Fi 2 Use Macroclass ID Fig 16 4 Classification algorithm Threshold optional allows for the definition of a classification threshold for all the spectral signatures for individ for Minimum Distance pixels are unclassified if distance is greater than threshold value for Maximum Likelihood pixels are unclassified if probability is less than threshold value max 100 for Spectral Angle Mapping pixels are unclassified if spectral angle distance is grea
182. ssessment land cover change classification di selector report This plugin requires the installation of GDAL OGR Numpy SciPy and Matplotlib Also a virtual machine is avaiable http fromqistors blogspot com p semy automatc os html Keywords PEREA ii A NA ESA ol alat Classifica o da Cobertura do Solo Clasificaci n de la selenext Cobertura de la Tierra Classification de la Couverture du Sol knaccubukauna sewnenonesosan Klassifizierung der Landbedeckung Classificazione della Copertura del Suolo For more information please visit http fromgistors blogspot com Yee rv dr 25 rating vote s 38811 downloads BY SENSUM Earth Observation Toots BY sc Diagram Downloader Tags raster Jandsat spectral signature classification land cover accuracy scatter plot supervised IB shepefie Encoding Fixer classification dos1 clip remote sensing mask analysis land cover change Ia de shews More info homepage tracker code repository B shortaut Manager Author Luca Congedo Ie Jt shotoobs t sirolereports T upgrade al instal plug Cose Help Plugin installed successfully o Search BE Remove emoty layers from the ml v ifirati n r pai Semi Automatic Classification Plugin B x a H ln Rel sy pain Plugin for the semi automatic supervised classification designed to expedite the Ras menu processing of multispectral or hyperspectral remote sensing images which provides a P RT Mapserver Ex
183. st2 7 MENSEM 42 veg 16 2 2 Veg 16 Veg 53 Soil 5 Soil2 3 Gye v2 ves Shr 6 0 Unclassified 6 Clouds 10 A 2 uo 28 cro Z3 Soil 7 Soil3 v le La SS bs 1 ll Export Import Add to signature ba m a B lass fication aigonthm F od y y Select classification algorithm Threshold Range radius Min ROI sze Max ROI width G Ca Maximum Ukelihood w 0 0000 da 0 006000 60 100 3 c 07 v Use Macroclass ID v Rapid ROI on band 1 m9 Class Automatic refresh ROI Automatic plot a Sue 500 3 Redo u o P e Show sparency _Redos J Show P v Display cursor for NDVI v m 1 Select qmi Reset Lo es RO nature definition i z T MC ID MC 8 Apply masi set 1 C1 Built up a LU _ y create vector _ Classification report ti IC info xe Perform classification 1 C Built upl Save RO v Add sig list pa SCP Classification Layers 1 legend entries removed 5 Coordinate 770185 1035563 Scale 1 1 451 348 v Rotation 0 0 C v Render EPSG 32616 Fig 20 21 Land cover classification 2 of the Landsat image LC80160532014057LGN00 166 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 You can see that this image covers the same area as the Landsat 8 image LC80150532014050LGN00 In fact we are going to use the classification of this Landsat 7 image in order to fill the Unclassified pixels of the Lan
184. ted within this table changes affect also the shapefile In order to highlight items perform a mouse selection in the table Table fields MC 1D ROI Macroclass ID int MC Info ROI Macroclass Information text C ID ROI Class ID int C Info ROI Class Information text Add to signature calculate ROI spectral signature from Input image pixels under ROI poly gon of highlighted ROIs in the table signatures are added to the Signature list page 95 if multiple high lighted ROIs share the same MC ID and C ID then only one spectral signature is calculated considering these ROIs as one polygon bX show the ROI spectral signature the Spectral Signature Plot page 135 spectral signature is calcu lated from the Input image ub open the Scatter Plot page 141 oO delete highlighted ROIs from the Training shapefile 15 3 ROI parameters ROI parameters are required for the ROI creation using a region growing algorithm Region growing works on the Input image defined in the Toolbar page 87 Range radius P set the interval which defines the maximum spectral distance between the seed pixel and the surrounding pixels in radiometry unit Min ROI size P set the minimum area of a ROI in pixel unit this setting overrides the Range radius until the minimum ROI size is reached if Rapid ROI on band is checked then ROI will 15 2 ROI list 91 Semi Automatic Classification Plugi
185. ter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Semi Automatic Classification Plugin IF Tools af Pre processing Jp Postprocessing Band calc ma Band set q it amonnan apanas al maman TR Downoad Landsat Mocc BL f E Search 1980 01 01 v fo 2015 08 15 v Ma x Satellites v Image list Download Fig 17 5 Download Landsat 17 1 Tools 109 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Reset directory reset the database directory to the default SCP installation directory Area coordinates Define the search area click the map for the definition of the Upper Left UL and Lower Right LR point coordinates X and Y of the rectangle defining the search area it is possible to enter the coordinates manually Search Define search settings such as the date of acquisition maximum cloud cover or search for specific Landsat images using the Image ID In addition it is possible to limit the search to certain Landsat satellites Acquisition date from to define the range of acquisition dates a narrow date range can make the search faster Max cloud cover define the maximum percentage of cloud cover present in the image Image ID search only the Image ID of Landsat images e g LC81910312015006LGNO0 it is pos sible to enter multiple Image IDs separat
186. ter Database Web SCP Processing Help S B RQAF SE vos AA As NOE E gt 4f Layers SCP ROI creation 9 Ww 216 0300 Training shapefile ae y s Newshp te g Unclassified I o Built up MC ID MC info co C info wv W RT_LE70150532014090EDC00_B5 Add to signature PLA 9 mo od 0 455221 aoe Range radius Min ROI size Max ROI width Vey v BF RT LE70150532014090EDCO0 B4 0 010000 60 100 Mo zx 0 5636 _ Rapid ROI on band 14 JF RT_LE70150532014090EDC00 B3 Fr d y Automatic refresh ROI Automatic plot a 95 cres BF RT_LE70150532014090EDC00_B2 mo Redos 3 o Show D Di cursor for NDVI AR BF RT_LE7O150532014090 DC00 B1 o AAA Lo r gt Mo 0 349183 0 349183 MC ID MC info b 1 Macroclass 1 Cio C info 1 Class 1 SCP Classification Layers Y Add sig list 5 Coordinate 947894 1118843 Scale 1 517 858 v Rotation 0 0 y Render EPSG 32616 Fig 20 29 Classification 3 with masked clouds Project it View Layer Settings Plugins Vector Raster Database Web SCP Processing Help BBEOSIE dmi JAME e A AMP A ADO deli fe me gt V Layers x SCP ROI creation ox im O V 3 i1 y Y New shp E Q 0 Unclassified F 9 1 8uitup MCID MC Info co C info E fo gt a G v v JF RT_LC80150532014050LGN00_B2 2 0 En e a 0 65 oa a vw J RT_LC80150532014050LGN00 B10 ig va z je Add to signa
187. ter than threshold value max 90 e Use Macroclass ID if checked the classification is performed using the Macroclass ID code MC ID of the signature if unchecked then the classification is performed using the Class ID code C ID of the signature i open the Signature threshold page 107 for the definition of signature thresholds 16 4 Classification preview Classification preview Size 100 gt MRE po e Show Transparency Fig 16 5 Classification preview Classification previews are temporary classifications of part of the input image every pixel has a value that repre sents a class Also a algorithm raster can be displayed with a click on the map algorithm raster represents the distance of the classified pixel to the corresponding signature every pixel has a value calculated by the algorithm with the spectral signature algorithm raster is useful for assessing how much a pixel classified as class X is dis tant from the corresponding spectral signature X black pixels are distant from the spectral signature and white pixels are closer After the creation of a new preview old previews are placed in QGIS Layers inside a layer group named Class_temp_group custom name can be defined in Temporary group name page 131 and are deleted when the QGIS session is closed e lt Size gt size in pixel unit of a classification preview i e the side length of a square centred at the clicked pixel
188. th QGIS Export links export the download links to a text file Download images from list start the download process of all the images listed in Sentinel images page 113 only if preview in Layers if checked the download is performed only for the images listed in Sentinel images page 113 that are also displayed as previews in the map Load bands in QGIS if checked bands are loaded in QGIS after the download 17 2 Pre processing The Pre processing tab allows for the manipulation of images before the actual classification process 17 2 1 Landsat Semi Automatic Classification Plugin YO Q9 A duci Pre processing db Postprocessing Band cale mm Band set s LA Z ERA EE Metadata Fig 17 8 Landsat The tab Landsat allows for the conversion of Landsat 1 2 and 3 MSS and Landsat 4 5 7 and 8 images from DN i e Digital Numbers to the physical measure of Top Of Atmosphere reflectance TOA or the application 114 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 of a simple atmospheric correction using the DOS1 method Dark Object Subtraction 1 which is an image based technique for more information about the Landsat conversion to TOA and DOS1 correction see Landsat image conversion to reflectance and DOS1 atmospheric correction page 47 Pan sharpening is also available for more information read Pan sharpen
189. ting vote s 38811 downloads Tags raster landsat spectral signature classification land cover accuracy scatter plot supervised classification dos clip remote sensing mask analysis land cover change More tracker code repository Author Luca Congedo The SCP should be automatically activated however be sure that the Semi Automatic Classification Plu gin is checked in the menu Installed the restart of QGIS could be necessary to complete the SCP installation Semi Automatic Classification Plugin Plugin for the semi automatic supervised classification designed to expedite the processing of multispectral or hyperspectral remote sensing images which provides a set of tools for pre processing and post processing Written by Luca Congedo the Semi Automatic Classification Plugin SCP allows for the semi automatic supervised dassification of remote sensing mages providing tools to expedite the creation of ROIs training cre l Sp tures es EE p semi 0S MEM at asai Classifica o da Cobertura do Solo Clasificaci n d Cobertura de la Tierra Classification de la Couverture du Sol knaccndua wn SeNinenoneaosaWan Klassifizierung der Landbedeckung Classificazione della Copertura del Suolo For more information please visit http iromaystors blogspot com Yero vtr 25 rating vote s 38811 downloads 2 3 Configuration of the plugin Now the Semi Automatic Classification Plugin is installed and two docks and
190. tion Release 4 8 0 1 Search END BE Remove empty layers from the ml z z c z E p essen Semi Automatic Classification Plugin El Not installed x plugin iot installe a Rosd graph Plugin for the semi automatic supervised classification designed to expedite the pesas di rss meru processing of multispectral or hyperspectral remote sensing images which provides a ORT D RT mapserver Exporter set of tools for pre processing and post processing Press RT Omero Sens Written by Luca Congedo the Semi Automatic Classification Plugin SCP allows for the semi automatic BETON supervised classification of remote sensing images providing tools to expedite the creation of ROIs training RuGeocoder areas through region growing or multiple ROI creation The spectral signatures of training meh i in be m automatically calculated and displayed in a spectral signature plot It is possible to import spectr Sample x signatures from external sources Also a tool allows for the selection and download of spectral vow SaTsviz from the USGS Spectral Library http speciab cr usgs gov spectral lib html Several tools are available for B scosurrer the pre processing phase image clipping Landsat conversion to reflectance the classification process Minimum Distance Maximum Likelihood Spectral Angle Mapping algorithms and classification previews Bl Search a format esc CAS Plugn and the post processing phase conversion to vector accuracy a
191. tion 9 me W ope Save Reset PO Y New shp Sg S MCID MCinfo CID Cinfo Color MCID MCinfo CID C Info SE A X v1 Water 1 Lake 11 Water 1 Lake 2 v 2 Buitup 2 Buildings 22 Built up 2 Buildings v 3 Vegeta 3 Vegeta 3 3 Vegetation 3 Vegetation D 4 v 4 Baresol 4 Bare soil 24 4 Bare soil 4 Bare soil he la s leo Le let LE export Jl import C Classification algorithm a Viv Select classification algorithm Threshold Ca Minimum Distance v 0 0000 I 2 __ Use Macroclass ID gnature definition meo Mcifo SSCS 1 Macroclass 1 cio C info 1 Class 1 ES Save ROI M Add sig list eundo 307796 4640117 1117682 v 11 6 Create the ROIs We are going to create several ROIs using the Macroclass ID defined in the following table Macroclass name Macroclass ID Water 1 Built up 2 Vegetation 3 Bare soil 4 11 6 Create the ROIs 73 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 In the Toolbar page 87 select the item 3 2 1 which is natural color in the list RGB After a few seconds the Color Composite page 35 will be displayed We can see that urban areas are white and vegetation is green TIP If a Band set page 127 is defined a temporary virtual raster named band set vrt is created automatically which allows for the display of Color Composite page 35 In order to speed up the visualization you can show only
192. to cite Congedo Luca Munafo Michele Macchi Silvia 2013 Investigating the Relationship between Land Cover and Vulnerability to Climate Change in Dar es Salaam Working Paper Rome Sapienza University Available at http www planning4adaptation eu Docs papers 08_NWP DoM_for_LCC_in_Dar_using_Landsat_Imagery pdf License Except where otherwise noted content of this work is licensed under a Creative Commons Attribution ShareAlike 4 0 International License Semi Automatic Classification Plugin is free software you can redistribute it and or modify it under the terms of the GNU General Public License as published by the Free Software Foundation version 3 of the License Semi Automatic Classification Plugin is distributed in the hope that it will be useful but WITHOUT ANY WARRANTY without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE See the GNU General Public License for more details You should have received a copy of the GNU General Public License along with Semi Automatic Classification Plugin If not see http www gnu org licenses I The first version of the Semi Automatic Classification Plugin was written by Luca Congedo for the Adapting to Climate Change in Coastal Dar es Salaam Project http www planning4adaptation eu Contents 1 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 2 Contents Part I Plugin
193. tt up1 E SCP Classification Layers _Save ROI Y Add sig list e Coordinate 946690 1094747 Scale 1 517 858 v Rotation 0 0 y Render QEPSG 32616 Fig 20 23 Land cover classification 3 of the Landsat image LE70150532014090EDC00 F 3 z ma s z E 7 Ep uy Semi Automatic Classification Plugin lt 2 gt d Y Q9 EF Tools E Pre processing a Post processing Band calc amp Band set Settings 7 About Band list E Raster bands Variable i Band name de 1 rasterl classification_1 j S Refresh list 2 raster2 RT_LC80150532014050LGN00_B7 1 se E raster3 RT LC80150532014050LGN00 B6 M RT LC80150532014050LGNO0 B5 RT LC80150532014050LGNO00 B4 ae sin asin RT LC80150532014050LGNO0 B5 RT LC80150532014050LGNO0 B4 em e r I cos acos es La La tan atan Lem Lin mJ np where Outp j _ Use NoData value 0 Extent Intersection Same as classification 1 v Calculate For other functions see http docs scipy org doc numpy reference routines math html and insert the function in the expression with prefix np e g np loglO raster1 np where rasterl gt 1 1 0 Show docks Z2 Quick user guide 73 Online help Close I Fig 20 24 NDVI calculation 168 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Releas
194. ture S A A ay ROI parameters ag Ve Range radius Min ROI size Max ROI width D 0 010000 60 100 c Rapid ROI on band 14 Automatic refresh ROI Automatic plot a ROI creation EMOL JP y Display cursor for NDVI v nature definition 2 MC iD MC info 1 Macroclass 1 UJ cio C info d 1 class 1 SCP Classification Layers E v Add sig list b 5 Coordinate 942083 1010168 Scale 1 1 447 598 v Rotation 0 0 iv Render QEPsG32000 Fig 20 30 Classification 1 with clouds masked using the alternative method 172 Chapter 20 Tutorial Land Cover Classification and Mosaic of Several Landsat images Semi Automatic Classification Plugin Documentation Release 4 8 0 1 20 6 Mosaic of Classifications In order to create a mosaic of classifications we are going to write an expression that will fill Unclassified pixels of the Landsat 8 image ID LC80150532014050LGN00 with the classification of the Landsat 7 image ID LE70150532014090EDCO00 Also we are going to merge these classifications to third one the Landsat 8 image with ID LC80160532014057LGNO0 In QGIS open the three cloud masked classifications Copy the following Expression page 126 in Band calc page 125 np where classification 1 clouds 0 np where classification 3 clouds 0 Uncheck the checkbox Intersection in Output raster page 127 and click Calculate The result e g classificatio
195. u is available in the Menu Bar of QGIS It is possible to move the Toolbar page 87 and the docks according to your needs as in the following image 20 Chapter 4 Installation in Debian Linux Semi Automatic Classification Plugin Documentation Release 4 8 0 1 d la s b Le kg L L Eeport import 2 left Minimum Distance y 0 0000 di Size 200 The configuration of available RAM is recommended in order to reduce the processing time From the SCP menu page 85 select QA Settings gt Processing In the Settings page 129 set the Available RAM MB to a value that should be half of the system RAM For instance if your system has 2GB of RAM set the value to 1024MB 4 3 Configuration of the plugin 21 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 22 Chapter 4 Installation in Debian Linux CHAPTER 5 Installation in Mac OS 5 1 QGIS download and installation Download and install the latest version of QGIS and GDAL from here In addition download and install the python modules Numpy Scipy and Matplotlib from this link Now QGIS 2 is installed o Qcis 006060 Project Edit View Layer Settings Plugins Vector Raster Help 238730 gt Oo ea j P 2 E m E t DmpBER xqye9 5257292p103i mg99 5 ut BB s 24 B Bw s B e P x As mq Ba Ber Layers Ba EA Vo i e A a Yo 2 Vo Coordinate 1 193 0 172 Scale 1 453 774 W Rend
196. ul for the classification process the pre processing of images and the post processing of land cover classifications Three buttons are always available Show docks show the ROI Creation dock page 89 and the Classification dock page 95 if closed Quick user guide open the online user manual in a web browser Online help open the Online help in a web browser also a Facebook group and a Google Commu nity are available for sharing information and asking for help about SCP P Configuration stored in the active project of QGIS Q Configuration stored in QGIS registry Following the list of tabs and the description thereof 101 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Tools page 102 Multiple ROI Creation page 102 USGS Spectral Library page 104 Algorithm band weight page 106 Signature threshold page 107 Download Landsat page 108 Download Sentinel page 112 Pre processing page 114 Landsat page 114 Clip multiple rasters page 116 Split raster bands page 117 Post processing page 117 Accuracy page 117 Land cover change page 120 Classification report page 121 Classification to vector page 121 Reclassification page 124 Band calc page 125 Band list page 126 Expression page 126 Output raster page 127 Band set page 127 Band list page 128 Band set definition
197. ut destination and start the calculation the error matrix is displayed in the tab frame and the error raster is loaded in QGIS 17 3 2 Land cover change o Semi Automatic Classification Plugin VOY 1 Fig 17 12 Land cover change 120 Chapter 17 Main Interface Window Semi Automatic Classification Plugin Documentation Release 4 8 0 1 The tab Land cover change allows for the comparison between two classifications in order to assess land cover changes Output isa land cover change raster ie a tif file showing the changes in the map where each pixel represents a category of comparison i e combinations between the two classifications which is the ChangeCode in the land cover change statistics and a text file containing the land cover change statistics i e a csv file separated by tab with the same name defined for the tif file The following video shows this tool http www youtube com watch t 834 amp v TCBpKvr3AI8 Classification input e Select the reference classification select a reference classification raster already loaded in QGIS Select the new classification select a new classification raster already loaded in QGIS to be compared with the reference classification Report unchanged pixels if checked report also unchanged pixels having the same value in both classifications e Calculate land cover change choose the output destination and start the calculation the land
198. views In the Classification algorithm page 97 select the classification algorithm Maximum Likelihood In Clas sification preview page 98 set Size 500 click the button and then left click in the map in order to create a classification preview Use the Transparency tool for changing the preview transparency and display the classification over the image In the Classification algorithm page 97 click the button and then right click in the map for calculating the algorithm raster The algorithm raster represents the calculation result of the Classification Algorithms page 40 it is useful for locating where we need to create new ROIs As shown in the following figure the algorithm raster has a grey scale symbology where dark areas represent pixels that the algorithm found distant from all the spectral signatures and white areas represents pixels that are very similar to spectral signatures In these dark areas we have a greater level of uncertainty therefore we need to create new ROIs in order to improve the classification results We can notice the presence of clouds in the image In order to avoid classification errors we need to mask clouds 20 3 Classification of Landsat Images 161 Semi Automatic Classification Plugin Documentation Release 4 8 0 1 Jeffries Matusita distance MC ID 1 MC info Built up C ID 1 C info Built up MC ID 2 MC info Vegetation C ID 2C info Trees 1 99973917846

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