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The Spectral Image Processing System (SIPS) Interactive

1. make_bbl make_hist make_sips_cube make_slb read_header rotate_cube subset_sips make_gainoff dn2ref flat_field TAR _calibrate Tape Utilities Reads an image cube with or without VICAR labels from an AVIRIS tape all present JPL AVIRIS tape formats are supported Reads the wavelength and FWHM data from an AVIRIS tape and outputs wavelength file this wavelength file is used as input to SIPS_View Displays the VICAR label information for each tape file File Utilities Converts the storage order of an input cube that is in BSQ BIP or BIL to either BSQ BIP or BIL format Creates an output cube with a standard SIPS header from BSQ BIP or BIL formatted non SIPS data with any size header Creates an output cube with a standard Terra Mar header used in Terra Mar MICROIMAGE software from a BSQ input cube Creates a bad bands file the bad bands file can be used as input to SIPS_View to mask out bad bands during spectral processing Creates a histogram file from an input BSQ cube file with a standard SIPS header the output file is used as input to SIPS_View for rapid contrast stretching Creates a BSQ formatted output cube with a standard SIPS header from multiple non SIPS input image files the output cube can be used as input to most other utilities and to SIPS_View Creates a SIPS spectral library file containing any number of ASCII spectra files Looks for a standard SIPS header in the input file and prints out the hea
2. Address correspondence to F A Kruse CSES CIRES Univ of Colorado Boulder CO 80309 0449 Received 25 January 1992 revised 31 October 1992 0034 4257 93 6 00 Elsevier Science Publishing Co Inc 1993 655 Avenue of the Americas New York NY 10010 operational techniques for quantitative analysis of imaging spectrometer data and to make them avail able to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System EOS High Resolution Imaging Spectrometer HIRIS INTRODUCTION Maps of the distribution and composition of ma terials on the Earth s surface are an important source of information for scientific investigations of resources environment and man made change on our planet During the late 1980s and early 1990s imaging spectrometry has emerged as an exciting technology that provides the potential for rapidly producing both traditional surface cover maps and new maps based on quantitative mea surement of Earth surface properties Imaging spectrometers acquire images simultaneously in many narrow contiguous spectral bands Goetz et al 1985 The data can be thought of as a cube of the dimensions lines x samples x bands NASA s operational imaging spectrome 145 146 Kruse et al ter the Airborne Visible Infrared Imaging Spec trometer AVIRIS presently acquires data in up to 224 spectral bands Vane et al 1993 Data
3. is a normal text widget on the lower right hand corner of the SIPS_View main window that dis Table 3 Definition of Widget Types and Actions Button widget Used to select a given option It consists of a rectangular region with a label Pushing the button by moving the mouse cursor over the button and pressing the left mouse button generates an event Used to select a value from a range of possible values It consists of a rectangular region in side of which is a sliding pointer that displays the current value The slider is grabbed by placing the mouse cursor over the slider pointer and holding down the left mouse button Moving the mouse while continuing to hold the mouse button down will change the slider value Slider widget Used to select one or more items from a given list It consists of a rectangular region with a list of items each with its own toggle button with an on and off state Menu widget Pull down Menu widget Used to select a given option from a list of options It consists of a button widget that when pressed expands into the list of choices The menu may be viewed or the menu item executed by moving the mouse cursor into the menu button and clicking the left mouse button Used to select one item from a list of items It consists of a rectangular region with a list of items one item per line Moving the mouse cursor over an item and clicking the left mouse button generates an ev
4. or select ing individual pixels within the selection window After the class regions are defined SIPS_View extracts the spectra for all the pixels contained within the regions and calculates the mean stan dard deviation and minimum and maximum spec tra The results of the calculations are plotted in the Saved Spectra Window This process may be repeated any number of times on separately defined regions in the image Every time a new region is defined and extracted the computed statistics for that region are averaged with the overall statistics for all the regions previously de fined for that class For each individual class the control panel shows the class name and the total number of pixels contained in all the polygon regions defined for that class The user can toggle between showing statistically derived spectra for a single class or showing the mean spectra of all classes in the Saved Spectra Window SIPS_ View also allows saving to disk files as either ASCII spectra or binary spectral library files the user s choice of the following e the coordinates of all of the pixels in a given class e the mean spectrum 154 Kruse et al SIPS View S Reflectance offset for clerity ectra Library Browsing Utili Sor or e COD2005 U8SG i 4 4 LOCATION 424 220 Wavelength micrometers JJ Figure 5 SIPS View Spectra Window showing three laboratory spectra illite i1107 usg dolomit
5. Inc 1991 IDL User s Guide Version 2 2 Boulder CO Roberts D A Yamaguchi Y and Lyon R J P 1985 Calibration of Airborne Imaging Spectrometer data to percent reflectance using field spectral measurements Spectral Imaging Processing System 163 in Proceedings Nineteenth International Symposium on Remote Sensing of Environment Environmental Research Institute of Michigan ERIM Ann Arbor MI 21 25 October Singer R B 1981 Near infrared spectral reflectance of mineral mixtures systematic combinations of pyroxenes olivine and iron oxides J Geophys Res 86 7967 7982 Smith M O and Adams J B 1985 Interpretation of AIS images of Cuprite Nevada using constraints of spectral mixtures in Proceedings AIS Workshop 8 10 April JPL Publication 85 41 Pasadena CA pp 62 67 Terra Mar 1991 User s Guide to Microlmage Software Ver sion 4 0 Terra Mar Resource Information Systems Inc Mountain View CA Torson J M 1989 Interactive image cube visualization and analysis in Proceedings Chapel Hill Workshop on Volume Visualization 18 19 May University of North Carolina Chapel Hill Vane G Ed 1987 Imaging spectroscopy II in Proceed ings 31st Annual International Technical Symposium So ciety of Photo Optical Instrumentation Engineers SPIE Bellingham WA Vol 834 232 pp Vane G Ed 1988 Proceedings of the Airborne Visible Infrared Imaging Spectrometer AVIRIS
6. Performance Evaluation Workshop JPL Publication 88 38 Pasadena CA 235 pp Vane G and Goetz A F H Eds 1985 Proceedings of the Airborne Imaging Spectrometer AIS Data Analysis Workshop 8 10 April JPL Publication 85 41 Pasadena CA 173 pp Vane G and Goetz A F H Eds 1986 Proceedings of the 2nd Airborne Imaging Spectrometer AIS Data Analysis Workshop 6 8 May JPL Publication 86 35 Pasadena CA 212 pp Vane G Green R O Chrien T G Enmark H T Hansen E G and Porter W M 1993 The airborne visible infrared imaging spectrometer AVIRIS Remote Sens Environ 44 127 143
7. and Calvin W M 1987 Imaging spectrom etry Spectral resolution and analytical identification of spectral features in Proceedings Society of Photo Optical Instrumentation Engineers SPIE Vol 834 pp 158 165 Goetz A F H and Davis C O 1991 High Resolution Imaging Spectrometer HIRIS science and instrument Int J Imaging Syst Technol 3 131 143 Goetz A F H Vane G Solomon J E and Rock B N 1985 Imaging spectrometry for earth remote sensing Science 228 1147 1153 Golub G H and Van Loan C F 1983 Matrix Computa tions John Hopkins University Press Baltimore MD Green R O Ed 1990 Proceedings of the Airborne Visi ble Infrared Imaging Spectrometer AVIRIS Workshop JPL Publication 90 54 Pasadena CA 280 pp Grove C I Hook S J and Paylor E D 1992 Laboratory reflectance spectra of 160 minerals 0 4 to 2 5 microme ters JPL Publication 92 2 Pasadena CA Huntington J F Green A A and Craig M D 1986 Preliminary geological investigation of AIS data at Mary Kathleen Queensland Australia in Proceedings 2nd Air borne Imaging Spectrometer AIS Data Analysis Work shop 6 8 May JPL Publication 86 35 Pasadena CA pp 109 131 Jet Propulsion Laboratory 1991 Planetary Data System Data Preparation Workbook Volume 1 Procedures Ver sion 2 0 Pasadena CA Kruse F A 1987 Extracting spectral information from imaging spectrometer da
8. for areas showing color differences and save to spectra library examine spectra using View Spectra op tion load spectral libraries and compare to determine minerals and other materials use image endmembers in saved image spectral library to perform SAM analysis within SIPS_View e select endmembers from library e edit spectral ranges e view results using SAM viewer e use sliders to highlight areas of high match save to SAM results cube exit SIPS_ View and reload SAM results cube as color image to show mixtures save results to files for filmwriter display single endmembers as density sliced images and save to files for film writer Analysis Reexamine endmembers based on results of SAM analysis using BSQ and BIP cubes in SIPS_View Unmix image using unconstrained un mixing e select endmembers e evaluate degeneracy of the library and adjust if required e unmix e evaluate abundance images error im ages and sum e revise endmembers if required Unmix image using constrained unmixing e reselect endmembers if required e evaluate degeneracy of the library and adjust if required e unmix e evaluate abundance images error im ages and sum e revise endmember images if required rerun unmixing if required Output import gray scale images color compos ites SAM analysis results images unmix ing results images into standard image processing software or IDL for further image analysis classification statistics
9. in the 0 4 2 5 um range however spectral resolution information for this specific instrument has not been deter mined The PIMA spectra are sampled at 2 nm in the 1 3 2 5 um range While the spectral reso lution function is not yet available for this instru ment comparison to measurements from the other spectrometers indicates that resolution is better than 4 nm throughout the measured spec tral range All spectra were measured with halon as the reference and reduced to absolute re flectance using a NBS halon spectrum A sixth IGCP 264 spectral library consists of the library spectra measured on the USGS spectrometer re sampled to 1989 AVIRIS wavelengths data prior to 20 September 1989 Spectral Slices Stacked Color Coded Spectra Extraction of spectral slices from the images allows display of spectral data as color coded stacked spectra Marsh and McKeon 1983 Kruse et al 1985 Huntington et al 1986 Fig 3 The color slice uses a standard 18 level density slice where white and red correspond to high intensity values and black and blue correspond to low intensity values SIPS_View is able to extract three different types of slices horizontal vertical and arbitrary A horizontal slice extracts spectra along a horizontal line in the image A vertical slice extracts spectra along a vertical line in the image Fig 3 An arbitrary slice extracts spectra along an arbitrary user defined path Each ex tracted slice occu
10. locate and extract polygons for calibration areas build spectral library containing ground spectra for the calibration areas using make_slb use empirical line method to calculate gains and offsets for calibration to re flectance with make_gainoff calibrate to reflectance using dn2ref to apply gains and offsets calculate histogram parameters using make_hist view spectra from the calibrated cube us ing make_bbl and interactively select bad bands rotate image 180 to north using rotate_cube make BIP cube using convert to allow efficient spectral viewing and analysis Interactive Viewing and Analysis in SIPS_View e display gray scale image use Spectra Browse function to evaluate spectral character of images select polygons containing calibrated light and dark targets load into View Spectra and compare to ground spectra used for calibration validate calibration browse through several gray scale images using the slider bar to look for spectral differences produce a variety of color images based on absorption bands of known materials to locate areas with absorption features extract spectral slices to evaluate spectral changes along specific traverses use histograms and linear stretches to pro duce enhanced images and save to color PostScript files produce color printer quicklook copies for reference use Spectra Browse function to examine individual spectra and the Spectra Aver age function to extract average spectra
11. minerals J Geophys Res 95 B8 12 653 12 680 Elvidge C D 1988 Vegetation reflectance features in AVIRIS data in Proceedings International Symposium on Remote Sensing of Environment Sixth Thematic Confer ence Remote Sensing for Exploration Geology Houston TX 16 19 May Environmental Research Institute of Michigan Ann Arbor pp 169 182 Gao B C and Goetz A F H 1990 Column atmospheric water vapor and vegetation liquid water retrievals from Airborne Imaging Spectrometer data J Geophys Res 95 D4 3549 3564 Geophysical and Environmental Research GER 1988 Single beam visible InfraRed Intelligent Spectroradi ometer SIRIS User s Manual GER Millbrook NY Goetz A F H 1981 Spectroscopic remote sensing for geological applications in Proceedings Society of Photo Optical Instrumentation Engineers SPIE Bellingham WA Vol 268 pp 17 21 Goetz A F H 1984 High spectral resolution remote sensing of the land in Proceedings Society of Photo Optical Instrumentation Engineers SPIE Bellingham WA Vol 475 pp 56 68 Goetz A F H and Boardman J W 1989 Quantitative determination of imaging spectrometer specifications based on spectral mixing models in Proceedings IGARSS 89 12th Canadian Symposium on Remote Sensing Vol 2 IGARSS Canada pp 1036 1039 Available from the Institute of Electrical amp Electronics Engineers Piscata way NJ 08854 Goetz A F H
12. the uncon strained solution endmember showing the derived spatial patterns of abundance for that endmember The additional two images are useful in assessing the uncertainty in the unmixing results They are 1 an image of the sum of the abundances at each pixel and 2 the root mean square RMS error at each pixel The error image displays how well the mixing library can be used to model each observed spec trum and can be used to assess the validity of the mixing library If contiguous regions of high error exist a required mixing endmember was probably omitted Refinement of the results involves itera tive unmixing with revised libraries until the RMS errors are low The resulting abundance images comprise estimates of the spatial distribution of the mixing endmember materials TYPICAL USER SCENARIO The following is a typical SIPS user scenario for AVIRIS data acquired for geologic investigations Many of the steps are common to analysis of any type of imaging spectrometer data and illustrate some of the relations between the different parts of SIPS The scenario is presented sequentially in the normal order executed in outline format 160 Kruse et al to clearly show the logical progression of the steps involved in the analysis Data Preparation receive and read AVIRIS tape using rd_avimage rd_avwave getting BSQ im age and wavelength files view radiance images using SIPS_View to verify data location and quality
13. 1988 it uses a raw radiance image file and outputs a calibrated apparent reflectance cube The remainder of the SIPS utilities operate on image data to prepare them for input to various image processing software For example make_ sips_cube and cvt2sips create an image file with a standard SIPS header As another example the cvt2terra_mar utility converts an image file with any type of header to an image file that can be used with the Terra Mar MicroImage software Terra Mar 1991 SIPS_VIEW General SIPS_View is an interactive IDL program that allows the user to visualize and work with imaging spectrometer data both spectrally and spatially It uses widgets along with mouse and keyboard input to create a user friendly interface A widget is a simple graphical object such as a pushbut ton slider or menu that allows users easy interac tion with the program A more detailed descrip tion of widgets is given in Table 3 Interaction by the user on a given widget produces what is re ferred to as an event from that widget When the user generates an event by pushing a button moving a slider etc the software is then able to respond to the event by performing some func tion An example of the SIPS_View main window using IDL widgets under Motif OSF 1989 is shown in Figure 2 In addition to the main window SIPS_ View creates and manages many other windows throughout its execution The Status Window
14. 1991 1993b and optionally displays the singular values of this li brary matrix The library s degeneracy is deter mined by examining the products of the decompo sition If the library of endmembers consists of completely spectrally separable orthogonal end members the normalized singular values will all be equal For a wholly degenerate library all but one singular value will be zero indicating that all of the endmembers are linearly scaled versions of each other At this stage the user can choose to start again and revise the library Finally once the user is satisfied with the library selected the program processes the full image data cube one line at a time The unconstrained Unmix program runs in about 1 h for a standard AVIRIS scene while the fully constrained Unmix program takes about 5 h times for five endmembers on a DEC Station 5000 200 The output of the unmixing process is another image data cube It has the same spatial dimen sions as the input data The number of output bands is equal to the number of endmembers plus two This cube contains one image for each Spectral Imaging Processing System 159 A gt 0 and B gt 0 A B 100 or less i endmember B endmember A Figure 9 Sketch of the constrained inversion solution space for two endmembers Best fitting abundances must be positive and sum to unity or less The reconstruction fit error must be greater than or equal to that for
15. F A Lefkoff A B and Dietz J B 1993 Expert system based mineral mapping in northern Death Valley California Nevada using the Airborne Visible Infrared Imaging Spectrometer AVIRIS Remote Sens Environ 44 309 336 Marsh S E and McKeon J B 1983 Integrated analysis of high resolution field and airborne spectroradiometer data for alteration mapping Econ Geol 78 4 618 632 Mazer A S Martin M Lee M and Solomon J E 1988 Image processing software for imaging spectrometry data analysis Remote Sens Environ 24 1 201 210 NASA 1987 HIRIS High Resolution Imaging Spectrome ter science opportunities for the 1990s Earth Observing System Instrument panel report V IIc Naticnal Aero nautics and Space Administration Washington DC 74 pp Open Software Foundation Inc 1989 OSF Motif User s Guide Cambridge MA 56 pp Pieters C M 1990 Reflectance Experiment Laboratory Description and User s Manual RELAB Brown Univer sity Providence RI Porter W M and Enmark H T 1987 A system overview of the Airborne Visible Infrared Imaging Spectrometer AVIRIS Proceedings 31st Annual International Techni cal Symposium Society of Photo Optical Instrumentation Engineers SPIE Bellingham WA Vol 834 pp 22 31 Press W H Flannery B P Teukolsky S A and Vetter ling W T 1986 Numerical Recipes Cambridge Univer sity Press New York Research Systems
16. It is our hope that these tools will be useful across multiple disciplines and allow quantitative analysis that will lead to new scientific discoveries using imaging spectrometer data CSES is continuing to develop SIPS as a gen eral tool for analysis of imaging spectrometer data One of the main goals of this effort is to modu larize the program to allow users to add custo mized functions Spectral Imaging Processing System 161 SOFTWARE RELEASE SIPS is being released to organizations outside CSES for analysis of imaging spectrometer data such as that produced by AVIRIS To promote scientific use of these data SIPS will be provided free of charge or royalties to any organization interested in use of imaging spectrometer data CSES plans to continue development of these programs and retains the title and copyright to the software documentation and supporting ma terials Recipients of this software are required to execute a memorandum of understanding MOU provided by CSES that specifies in detail all of the associated conditions To get a copy of the software agreement contact Kathy Heidebrecht or Fred Kruse by electronic mail sips cses color ado edu phone 303 492 1866 or FAX 303 492 5070 HARDWARE SOFTWARE REQUIREMENTS SIPS runs on Unix based workstations under ei ther Motif or Openlook window managers in 8 bit color mode The platforms and the software ver sions on which SIPS has been tested are shown in Tabl
17. N CRITERIA The following requirements for the next genera tion of imaging spectrometer software were de fined based on an informal user survey and docu mented research needs of CSES scientists in a variety of disciplines e The system should allow routine analysis of imaging spectrometer data sets to mini mally include AVIRIS GERIS and Eos HIRIS e It should be flexible enough to permit lim ited analysis of other multispectral data sets such as Landsat MSS Landsat TM and SPOT e The system should provide utilities for in put of data data formatting data calibra tion and other common image processing tasks Spectral Imaging Processing System 147 e Data visualization tools should be pro vided for rapid exploratory analysis Numerical tools should be provided for quantitative modeling with the results dis played visually in real time e The tools and techniques provided should be generally useful across multiple disci plines The software should have a user friendly interface The software should be independent of specific image display hardware SIPS UTILITIES The SIPS utilities module contains tools that pre pare data for input to SIPS_View the analysis programs and other image processing software These tools are written in IDL with the exception of the tape reading utilities The tape utilities are either IDL programs that spawn processes written in the C programming language or are writt
18. REMOTE SENS ENVIRON 44 145 163 1993 The Spectral Image Processing System SIPS Interactive Visualization and Analysis of Imaging Spectrometer Data F A Kruse A B Lefkoff J W Boardman K B Heidebrecht A T Shapiro P J Barloon and A F H Goetz Center for the Study of Earth from Space CSES Cooperative Institute for Research in Environmental Sciences CIRES University of Colorado Boulder Department of Geological Sciences University of Colorado Boulder The Center for the Study of Earth from Space CSES at the University of Colorado Boulder has developed a prototype interactive software system called the Spectral Image Processing System SIPS using IDL the Interactive Data Language on UNIX based workstations SIPS is designed to take advantage of the combination of high spectral reso lution and spatial data presentation unique to im aging spectrometers It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling The user interface is X Windows based user friendly and provides point and click operation SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance sci entific results from imaging spectrometer data The objective of this continuing effort is to develop
19. age spectra and the laboratory spectra are the same magnitude 0 1 0 when plotted SIPS_View plots each spectrum in a different color up to 16 colors and the names of the displayed spectra are listed in matching color on the right side of the plot If a bad bands file is present and the bad bands filter is on then all spectra containing the same number of bands as the image data will be plotted with only the good bands showing Any spectrum containing a different number of bands will ignore the bad bands list The two areas in the left column below the plot titled Library Spectra Files and ASCII Spectra Files are used to select library files and ASCII spectra files to plot Fig 5 Both areas contain an editable text widget titled Path and a list widget below listing the matched files of the path Moving the mouse cursor over the desired file name in the ASCII Spectra Files list and clicking the left mouse button causes the selection to be highlighted and the spectrum to be read from disk and plotted Moving the mouse cursor over the desired file name in the Library Spectra Files list and clicking the left mouse button high lights the selection and opens the library file SIPS_View displays this library as the Current Library and lists the first 256 elements of this library in the middle column Moving the mouse cursor over the desired element name in the list and clicking the left mouse button select and plots this
20. ced of the View Spectra plot exactly as it appears If the plotted spectra are stacked and smoothed with the FFT Filter then that is how the plot will be saved to the PostScript file Spectral Libraries SIPS_View spectral libraries are binary files that contain spectra and their associated wavelengths see the SIPS User s Guide CSES 1992 for format information They also have an associated auxil iary information file SIPS includes two sets of libraries of laboratory spectra Digital spectra for approximately 135 minerals are provided courtesy of Jet Propulsion Laboratory Grove et al 1992 The second set consists of digital spectra of 25 well characterized minerals each measured on five different spectrometers as part of Interna tional Geologic Correlation Project 264 IGCP 264 Remote Sensing Spectral Properties Kruse unpublished data The JPL spectra are hemispherical reflec tance measurements from 0 4 um to 2 5 um made on a Beckman UV5240 spectrophotometer The sampling interval is every 0 001 um 1 nm be tween 0 4 um and 0 8 um and 0 004 um 4 nm from 0 8 um to 2 5 um Spectral resolution is approximately 1 of the wavelength measured Two sets of three six total spectral libraries are provided corresponding to three grain sizes 125 500 um 45 125 wm and lt 45 um measured at JPL One set of the three libraries is provided at full resolution to allow use of this resolution or resampling to specific AVIRIS wav
21. ch reference spec trum separately as gray scale images Small spec tral angles correspond to high similarity and these pixels are shown in the brighter gray levels Larger angles corresponding to less similar spec tral shapes are shown in the darker gray levels Two interactive sliders titled Low Threshold and High Threshold can be used to fine tune the contrast stretch of the image Values between the two slider settings are stretched linearly be tween black and white Values outside this range are set to black if they have a spectral angle greater than the High Threshold setting less simi lar to the reference and white if they have a spectral angle less than the Low Threshold setting more similar to the reference In addition the two sliders can be locked one radian apart In the locked setting the image displayed is a binary map of all pixels more similar than the High Threshold SIPS ANALYSIS PROGRAMS The analysis module provides tools that perform complex calculations on an entire image and are too time consuming for interactive use Currently only the unmix analysis tool which performs linear spectral unmixing is available in this module A knowledge based expert system analysis utility is presently undergoing testing and revision Kruse 158 Kruse et al Spectral Angle Mapper Viewer Figure 7 SAM Viewer Window showing gray scale results for comparison of image spectra to a reference spectrum
22. collected by these instruments can be displayed and analyzed as either images or as detailed spec tra one spectrum for each picture element in the image High spectral resolution reflectance spectra collected by imaging spectrometers allow direct identification and characterization of indi vidual materials including minerals vegetation water ice and snow Goetz et al 1985 Vane and Goetz 1985 1986 Vane 1987 1988 Green 1990 NASA 1987 The strength of imaging spectrometry lies in the simultaneous use of spatial and spectral infor mation for integrated analysis Previous software packages the Spectral Analysis Manager SPAM Mazer et al 1988 developed at the Jet Propul sion Laboratory JPL and the Integrated Software for Imaging Spectrometers ISIS developed at the U S Geological Survey in Flagstaff Torson 1989 utilized this concept to permit interactive analysis of subsets of imaging spectrometer data While providing basic capabilities these packages did not satisfy many of the user community s sci entific requirements primarily because they did not provide utilities for preprocessing or calibra tion only allowed analysis of a small part of the image cube and were hardware specific Despite promising results from AVIRIS data using these and other software packages in the geological sciences terrestrial ecology hydrology and ocean ography scientists have not yet tapped the full potential of the data The v
23. corresponding to dolomite Brighter areas represent better matches et al 1993 and will be released in the next version of SIPS Other analysis modules are being developed and will be added at a later date Spectral mixing is a consequence of the mix ing of materials having different spectral proper ties within the GFOV of a single pixel Singer 1981 Smith and Adams 1985 Boardman 1991 The SIPS unmixing program written in IDL uses a simple linear mixing model This model assumes that observed spectra can be modeled as linear combinations of endmembers contained in a spec tral mixing library Boardman 1993b The un mixing approach seeks to determine the fractional abundance of each endmember within each pixel Given more bands than mixing endmembers the problem can be cast in terms of an overdeter mined linear least squares inversion for each im age spectrum Fig 8 Boardman 1989 1990 1991 The SIPS unmixing program provides three types of unmixing algorithms unconstrained par tially constrained and fully constrained Board man 1990 The unconstrained version provides a classic least squares solution to the unmixing problem and the derived abundances are free to take on any value including negative ones In the constrained versions the derived abundances are required to be nonnegative When fully con strained their sum must be unity or less Fig 9 Several steps are involved in using the unmix ing pr
24. der information if no header is found it prints an error Rotates the cube 90 180 or 270 this utility can be used to change the orientation of the image in the Image Window Extracts a subset of an input cube and writes it to a cube file with a standard SIPS header Calibration Utilities Uses the empirical line method using selected areas on the ground to calibrate the data to reflectance Roberts et al 1985 Elvidge 1988 Kruse et al 1990 This calibration method requires choosing two or more ground target regions with diverse albedos and acquiring field or laboratory spectra to characterize them make_gainoff uses the ground target reflectance spectra and the associated image radiance spectra to perform a linear regression for each band to determine the gains and offsets required to convert the DN values to reflectance The output is a file containing the gains and offsets that can be used as input to dn2ref Applies gains and offsets calculated by make_gainoff to the entire imaging spectrometer cube it uses a raw radiance image file and outputs a calibrated apparent reflectance cube Removes a single spectrum for an area selected by the user with a uniform spectral response from the entire cube by division it uses a raw radiance image file and outputs a calibrated apparent reflectance cube Removes the global average spectrum for a cube from the entire cube by division to calibrate to Internal Average Relative Reflectance IARR Kruse
25. display in that color causes SIPS_View to read and display the new color composite image when the slider is released Selecting density slice on the menu provides an 18 color RGB ramp for a single image where low brightness values are represented by blacks and blues and high brightness values are represented by reds and whites Individual ranges Spectral Imaging Processing System 151 Figure 3 Typical SIPS user session screen showing some of the functions used for spectral analysis of imaging spectrom eter data can also be manually selected for each color of the density slice The Zoom Window The Zoom Window located to the right of the Image Window Figs 2 and 3 displays a small area from the Image Window with a user defin able zoom factor applied The center of the zoomed area is defined by the position of the cursor in the Image Window The pixels displayed can be magnified from 1 to 16 times their original size by grabbing the slider titled Zoom Factor and changing its value to the desired new zoom factor The positions of the four red corner indica tors in the Image Window change to reflect the new area displayed in the Zoom Window The line and sample coordinates and data value of the current pixel are displayed under the Zoom Window Interactive Contrast Enhancement The user can change the contrast stretch for each band displayed by altering how the data fit into the 0 to 255 8 bit display range Th
26. e Contrast Stretch option is selected via the pull down Im age Menu button SIPS_View creates a new win dow for the enhancement functions Fig 4 This window contains three draw widgets and plots the current histograms of the red green and blue bands When the current image is a gray scale or density slice only the first window is used Next to each plot SIPS_View displays the band num ber and the minimum and maximum values used for the current stretch If there is a histogram file 152 Kruse et al Contrast Stretching 200 400 Reflectance Values 0 600 Reflectance Values 0 500 Reflectance Values Figure 4 SIPS Histogram Window showing options tor interactive contrast stretching o imaging spectrometer data associated with the input image then a slider is cally where the current minimum and maximum also provided that allows the user to choose a values lie on the histogram Whenever a change specific percent of the data to stretch 0 15 is made to the minimum and maximum stretch Two vertical bars within the plot display graphi values the position of the two vertical lines in the histogram plot changes as well The currently displayed image in the Image Window will not be updated with the new stretch however until one of the three Apply Stretch buttons is se lected When applied all values less than or equal to the minimum stretch value are set equal to 0 and all values greater than or equal to
27. e cod2005 usg and calcite co2004 usg and three spectra extracted from an imaging spectrometer cube for an area with sericite muscovite or illite pixel 481 366 calcite pixel 424 217 and dolomite pixel 424 220 The laboratory spectra are resampled to AVIRIS resolution Windows for interactive selection of both ASCII and binary libraries are an integral part of this utility e one standard deviation spectrum above the mean e one standard deviation spectrum below the mean e spectrum of the cumulative maximum at each wavelength spectrum of the cumulative minimum at each wavelength View Spectra View Spectra is a utility used for spectral display and analysis When the View Spectra function is selected SIPS_View creates a separate window to plot the spectra currently in the Saved Spectra Window as well as access and plot other ASCII and library spectra saved in binary format Fig 5 The user can then manipulate this plot in a number of different ways produce a PostScript output file of the plot or import the plotted spectra back into the Saved Spectra Window for subsequent use in other SIPS functions The plot within the View Spectra Window initially displays the spectra that are in the Saved Spectra Window Image spectra from the Saved Spectra Window are divided by a scale factor of 1000 the same scaling factor used by SIPS Utili ties to preserve precision in reflectance calibra tion so that both the im
28. e 1 IDL version 2 4 or higher is required While SIPS should work on any platform that supports IDL with widgets these are the only platforms that have been tested to date For more information concerning IDL contact Research Systems Inc 777 29th St Boulder CO 80303 SIPS was originally developed as a means for viewing and analyzing AVIRIS data Many of the ideas and techniques incorporated into SIPS are the result of nearly 10 years experience with imaging spectrometer analysis by principals at CSES The SPAM and ISIS software provided some impetus towards the types of analyses we wanted to perform and we would like to acknowledge this influence SIPS however was developed from scratch using the IDL programming language to satisfy specific analysis requirements not available in any existing software package The basic interactive package SIPS_View was developed under funding from NASA as part of the Innovative Research Program funded research proposal Artificial Intelligence for Geologic Mapping NASA Grant NAGW 1601 Dr F A Kruse Principal Investigator Addi tional support for documentation of SIPS and development of unmixing routines included as part of SIPS were supported respectively by NASA Grant NAS5 30552 Dr A F H Goetz Principal Investigator and by a NASA Graduate Research Fellowship Dr J W Boardman The interactive SIPS_View program version 1 0 was written by A B Lefkoff with version 1 1 addi
29. effects SIPS_View allows up to 10 reference spectra to be processed simultaneously using SAM Spec tra can be selected from SIPS spectral libraries ASCII spectra files or any spectra contained in the Saved Spectra Window Reference spectra must have the same wavelength set as the image to which they will be compared If a bad bands file is associated with the image cube then SAM will use the reference spectra with the bad bands Spectral Imaging Processing System 157 masked ignoring the bad bands in the calcula tions Optionally all bands can be included and SAM will perform its calculations on all the bands over the defined range For each reference spectrum chosen the spec tral angle a is determined for every image spec trum and this value in radians is assigned to that pixel in the output SAM image A unique spectral range may be chosen for each reference spectrum This allows the algorithm to focus on spectral regions that are significant for a particular refer ence spectrum The derived spectral angle maps form a new data cube with the number of bands equal to the number of reference spectra used in the mapping Results can be viewed immediately using the interactive SAM Viewer Fig 7 The dynamic nature of the viewing interface helps the user to analyze the spatial patterns of spectral variability in the image and to rapidly map areas that are spectrally similar The SAM Viewer Win dow displays the results for ea
30. elengths The second set of the three libraries is provided resam pled to 1989 AVIRIS wavelengths data prior to 20 September 1989 The IGCP 264 spectral libraries included in SIPS represent the prototype database of approxi mately 25 well characterized minerals identified 156 Kruse et al as critical for geologic mapping by a 1987 inter national survey Kruse unpublished data Five spectral libraries measured on five different spec trometers for the 25 minerals are provided The same samples were measured on a Beckman UV5270 spectrophotometer at CSES a Beckman UV5240 spectrophotometer at the U S Geologi cal Survey in Denver Clark et al 1990 on the RELAB spectrometer at Brown University Pieters 1990 on the SIRIS field spectrometer in the laboratory at CSES Geophysical and Envi ronmental Research 1988 and with the proto type of a new high resolution field spectrometer the PIMA II manufactured by Integrated Spec tronics Pty Ltd in the laboratory at CSES The CSES Beckman lab spectrometer measures at constant 3 8 nm resolution sampled at 1 nm throughout the 0 7 2 5 um range The USGS spectra are provided at the standard 1 x resolu tion ranging from 2 nm to 10 nm in the 0 4 2 4 um range and falling off to nearly 30 nm in the 2 4 2 5 nm range The RELAB spectra are pro vided at 2 13 nm resolution sampled at 5 nm in the 0 4 2 5 um range The SIRIS spectra are sampled from 2 nm to 5 nm
31. ementation SIPS was specifically designed to deal with data from AVIRIS and the High Resolution Im aging Spectrometer HIRIS NASA 1987 but has been tested with other data sets including the Geophysical and Environmental Research Im aging Spectrometer GERIS GEOSCAN images and Landsat TM It takes advantage of high speed disk access and fast processors running under the UNIX operating system Table 1 to provide interactive analysis of entire imaging spectrome ter data sets SIPS is specifically designed to allow analysis of single or multiple imaging spectrome ter data segments at full spatial and spectral reso lution It also allows visualization and interaction analysis of image cubes derived from quantitative analysis procedures such as absorption band char acterization and spectral unmixing SIPS version 1 1 presently consists of three modules SIPS Utilities SIPS_View and SIPS Analysis Fig 1 The SIPS Utilities are programs for disk to disk processing of imaging spectrome try data and include tape reading data formatting calibration to reflectance and cosmetic process ing of the data SIPS_View provides interactive visualization and analysis capabilities for large imaging spectrometer data sets It provides a user friendly interface through the use of the X Window system and widgets such as menus buttons and slider bars SIPS_View provides the capability to interactively select and enhance bands to make co
32. en entirely in C SIPS utilities operate on images with standard headers conforming to the Planetary Data System PDS format Jet Propul sion Laboratory 1991 The utilities all have a command line interface and some also have an interactive graphical interface A list and brief description of tools presently available is given in Table 2 Details of the header format including any variation from the PDS format and a complete list of parameters and detailed usage instructions for each tool are given in the SIPS User s Guide CSES 1992 Most of the SIPS utilities format image data or create files for input to SIPS_View For example when starting with AVIRIS data on tape the rd_avimage utility is used to create a band sequen tial BSQ and or band interleaved by pixel BIP and or band interleaved by line BIL cube from the raw radiance data on that tape The rd_avwave utility is used to create a wavelength table from the tape To derive apparent reflectance values from the raw radiance values using the empirical line calibration Roberts et al 1985 the make_ gainoff and dn2ref utilities are executed using the BSQ cube as input The data set for input into SIPS_View is completed using convert make_hist and make_bbl to create the calibrated BIP image cube a histogram file and a bad band list file 148 Kruse et al Table 2 Description of SIPS Utilities rd_avimage rd_avwave vicar_info convert evt2sips evt2terra_mar
33. ent and selects the item List widget Editable Text widget Used to receive user input from the keyboard It consists of a rectangular region that when activated will display and act on characters typed from the keyboard Normal Text widget Used to display text in a window It consists of a rectangular region usually surrounded by a frame where the program may display text Draw widget Used to display a standard IDL graphics window within a widget application It consists of a rectangular region where plots and images are displayed plays useful information about the current state of SIPS View and the functions that the three mouse buttons will currently perform The last two lines of the Status Window report processing status The Scroll Window not shown in Fig 2 displays a subsampled image if a data set is larger than the standard 512 line x 614 pixel AVIRIS image and allows extraction of a full reso lution image for a desired location The Image Window contains the image at full resolution The Zoom Window contains a subset of the image zoomed from 1 to 16 times The Current Spectral Imaging Processing System 149 Spectrum Window and Saved Spectra Window are used for viewing extracting and saving spec tra Other windows such as View Spectra Spec tral Slices and the SAM Viewer are created only when accessed by the menu fractions SIPS_View requires as a minimum
34. etc e produce hardcopy output using filmwriter Integrated Analyses e Register images to map base e transfer results to GIS system for further analysis with results from other images field mapping field spectra and labora tory analytical work CONCLUSIONS SIPS is an integrated software system for analysis of imaging spectrometer data SIPS is designed to take advantage of the inherent strength of im aging spectrometer data simultaneous high reso lution spectral measurements and spatial display It provides the basic capabilities to proceed from raw radiance data through calibration to inter active viewing and analysis to quantitative results and hardcopy output It provides utilities for input of data data formatting data calibration and other common image processing procedures Data visualization tools are provided for rapid exploratory analysis and numerical tools are pro vided for quantitative modeling SIPS makes possible routine display and anal ysis of a volume and complexity of information that up until now have detailed analyses difficult It has been used for analysis of imaging spectrom eter data from AVIRIS GERIS and GEOSCAN and to look at other multispectral data sets from Landsat MSS Landsat TM and SPOT The proto type interactive software system using IDL on UNIX based workstations simplifies analysis of imaging spectrometer data by allowing scientists to rapidly interact with entire datasets
35. image in a 512 line x 614 sample window with a default 2 linear contrast stretch applied The displayed image can be a gray scale or density sliced image of a specific band a color composite image of three bands or a gray scale image of analysis results An example of a typical SIPS user session screen is shown in Figure 3 Possible actions associated with the Image Window include selecting which band is displayed e selecting the display mode zoom factor and contrast stretch of the band displayed 150 Kruse et al 266 Sample 308 10 Vevelength I lt per Eora ts ore EE t da E y DE n o o am j ee ETS a Box S 2a Fevelengtb Figure 2 The initial view of the SIPS_View main window using IDL widgets under Motif e saving the current image to a data file or a color PostScript file Image Display Individual bands can be selected by entering the band number or wavelength or by using slider bars Fig 3 Grabbing and moving the slider changes its value and thus the band displayed SIPS View can display the current band as a gray scale or a density sliced image or display three bands as an RGB color composite Selecting Toggle Color On on the pull down Image Menu causes SIPS View to replace the single slider used for selecting a single band number with three sliders used for selecting a red green and blue band number Grabbing any of these sliders and selecting a new band number to
36. lor composite images It allows rapid extraction and display of individual spectra or of spectra extracted from polygon regions These spectra can be visually compared to library sips sips_util sips_view sips_anal convert image display unmix cvt2sips contrast enhancement unconstrained evt2terra_mar spectra browsing partially constrained dn2ref spectrum extraction fully constrained flat_field jar_calibrate make_bbl make_gainoff make_hist spectra averaging view spectra w libraries spectral slice extraction spectral matching make_sips_cube make_slb rd_avimage rd_avwave read_header rotate_cube subset_sips vicar_info Figure 1 Tree diagram showing functions of the Spectral Image Processing System SIPS Brief descriptions of the SIPS Utility functions are given in Table 2 spectra or automatically matched to spectral end members Extraction of spectral slices also allows display of spectral data as stacked color coded spectral images Marsh and McKeon 1983 SIPS Analysis is a set of programs for detailed full cube analysis of imaging spectrometry data These are primarily programs that require extensive mathe matical calculations and CPU time that are not amenable to interactive analysis on a complete data set Together these three modules provide the capabilities to proceed from raw radiance data to final analysis results and output SIPS DESIG
37. maximum stretch value are set equal to 255 All values be tween the minimum and maximum are stretched linearly into the 0 255 range Saving Images The displayed image can be saved to a file as byte scaled images of either a single band gray scale image or a three band RGB image with the cur rent color tables applied The output files are in BSQ format with the first band corresponding to the red values the second band to the green values and the third band to the blue values This allows easy data interchange and also provides contrast enhanced images for use with color film writers or other output devices SIPS_View also gives the user the option of saving files in color PostScript format SIPS_View Spectral Functions SIPS_View spectral functions are those items that deal with imaging spectrometer data primarily in its spectral format These functions include spec tra browsing spectra averaging spectral slice ex traction viewing spectra with spectral libraries and Spectral Angle Mapping SAM Spectra Browsing SIPS_View allows the user to move the mouse cursor around the Image Window displaying the current spectrum in real time If SIPS_View is being executed with either a BIL or BIP cube then the spectrum at the current cursor position is displayed in the Current Spectrum Window and continuously updated as the cursor is moved using the mouse Alternatively if only a BSQ cube is present then a click of the left mou
38. ogram Any user should become familiar with the concept of unmixing and its inherent assumptions and limitations before trying to un mix their data Singer 1981 Smith and Adams 1985 Boardman 1991 and references therein The first and most important step is the selection of the spectral mixing library endmembers In the SIPS unmixing procedure the mixing library is formed by interactively choosing members from the imaging spectrometer cube or from any num ber of spectral libraries using SIPS_View The unmixing library should contain all the materials believed to be mixing in the scene Conversely it should not contain members that are not present Once the mixing library is formed the endmem ber spectral are displayed and a subset of the full spectral range can be chosen to ignore noisy bands and any spectral variability in wavelength E wimuydeds pavresqo inverse ixing abundances endmember library ump ds p ss sqo Figure 8 Linear spectral mixing forward and inverse mod els If the number of endmembers in the library is less than the number of bands in the data then the problem is an overdetermined linear least squares inversion regions that are not of interest Bad bands can also be excluded using the bad bands file Once the type of unmixing constraints are chosen the program inverts the spectral library using singular value decomposition Golub and Van Loan 1983 Press et al 1986 Boardman
39. olume and complexity of information contained in the imaging spectrom eter data have made detailed analyses difficult and only a small fraction of the data collected have ever been analyzed to extract quantitative information The Spectral Image Processing System SIPS is a software package developed by the Center for the Study of Earth from Space CSES at the University of Colorado Boulder using IDL the Interactive Data Language a proprietary pro gramming language Research Systems Inc 1991 in response to a perceived need to provide integrated tools for analysis of imaging spectrome ter data both spectrally and spatially Many of the ideas and techniques incorporated into SIPS are the result of nearly 10 years experience with imaging spectrometer analysis by principals at Table 1 SIPS Version 1 1 Hardware Platforms and Required Software Versions Operating Window Hardware Color System Manager IDL DECstation 3100 8 bit Ultrix 4 1 Motif 1 1 IDL 2 2 2 DECstation 5000 8 b it Ultrix 4 1 Motif 1 1 IDL 2 2 2 IBM RISC 6000 8 bit AIX 3 1 Motif 1 1 IDL 2 2 2 SUN 8 bit SunOS 4 1 Openlook 2 0 IDL 2 2 2 SGI with xterm 8 bit IRIX 4 0 4Dwm IDL 2 2 2 CSES Goetz 1981 1984 Goetz et al 1985 Goetz and Calvin 1987 Goetz and Boardman 1989 Gao and Goetz 1990 Goetz and Davis 1991 Kruse et al 1985 1990 Kruse 1987 1988 Kruse and Dietz 1991 Boardman 1989 1990 1991 This manuscript describes the version 1 1 impl
40. one image cube in either BSQ BIL or BIP format to run Optimum functionality and performance are ob tained if both a BSQ and BIP file are present The second copy of the data in BIP format allows quick access to individual spectra Other optional files including a wavelength file histogram file bad band file and spectral libraries enhance the performance and utility of the program The wavelength file allows the SIPS_View to associate each band to a specific wavelength The histogram file allows the program to quickly perform stretches of 16 bit data to byte data for display The band band file allows users to mask out un wanted bands when plotting and analyzing spec tra File characteristics and formats are described in detail in the SIPS User s Guide CSES 1992 SIPS_View Display Functions Nearly all of the available functions in SIPS_View perform their operations in the Image Zoom Current Spectrum and Saved Spectra Windows located within the main SIPS_View window Fig 2 The display functions operate on the image data in its spatial format and are accessed by clicking on the pull down Image Menu When SIPS_View is started a gray scale image is dis played If the image is larger than 512 x 614 SIPS_View will display the full image subsampled to fit into the Scroll Window Scrolling is used to allow the user to view different portions of the image at full resolution The Image Window dis plays the full resolution
41. pies its own window Up to five slice windows may be open at any one time There are two general stages to using the slice option First SIPS_ View extracts the desired slices the user has defined and places them into their own windows Once these windows have been created the user can move the mouse cursor around in the slice windows and see a specific spectrum plotted in the Current Spectrum Win dow The line and sample position for the pixel associated with that spectrum are listed in the Slice Window as well as the band and reflectance value under the cursor The Zoom Window also follows along and updates the current pixel loca tion The Spectral Angle Mapper SAM The Spectral Angle Mapper SAM is a tool that permits rapid mapping of the spectral similarity of image spectra to reference spectra Boardman 1993a The reference spectra can be either labo ratory or field spectra or extracted from the image This method assumes that the data have been reduced to apparent reflectance with all dark current and path radiance biases removed The algorithm determines the spectral similarity be tween two spectra by calculating the angle be tween the two spectra treating them as vectors in a space with dimensionality equal to the num ber of bands nb A simplified explanation of this can be given by considering a reference spectrum and a test spectrum from two band data repre sented on a two dimensional plot as two point
42. s Fig 6 The lines connecting each spectrum point and the origin contain all possible positions for that material corresponding to the range of possible illuminations Poorly illuminated pixels will fall closer to the origin the dark point than pixels with the same spectral signature but greater illumination Notice however that the angle be tween the vectors is the same regardless of their length The SAM algorithm Boardman 1993a generalizes this geometric interpretation to nb dimensional space The calculation consists of taking the arccosine of the dot product of the spectra SAM determines the similarity of a test Band 2 Band 1 Figure 6 Plot of a reference spectrum and test spectrum for a two band image The same materials with varying illu mination are represented by the vectors connecting the or igin no illumination and projected through the points rep resenting the actual spectra spectrum t to a reference spectrum r by applying the following equation which can also be written as nb gt tiri 1 i l cos nb 1 2 ib 1 2 pP i l i l where nb number of bands This measure of similarity is insensitive to gain factors because the angle between two vectors is invariant with respect to the lengths of the vec tors As a result laboratory spectra can be directly compared to remotely sensed apparent reflec tance spectra which inherently have an unknown gain factor related to topographic illumination
43. se button causes the spectrum at the current cursor position to be extracted band by band and displayed in the Current Spectrum Window a process that takes 5 15 s per spectrum on a DECstation 5000 At any time while browsing a spectrum may be saved into the Saved Spectra Window by clicking the middle mouse button Once a spectrum is in Spectral Imaging Processing System 153 the Saved Spectra Window it may be saved to disk in an ASCII or binary library format further examined in the View Spectra Window or used as input for analyses Spectra Averaging This function allows the user to interactively de fine and extract spectra for irregularly shaped polygon regions vectors and individual pixels SIPS_View provides five totally independent classes Fig 2 Any one class can contain up to 10 000 pixels from one or more separately defined regions Only one class is active at a time and the statistics for that class are totally independent of the other four classes For example if a polygon is defined when Class 1 is active the spectra of the pixels contained within this polygon region will be averaged only with other regions defined for Class 1 The region of interest is selected by position ing the Zoom Window coverage and setting the zoom factor Selecting the define class button creates a new window with the same spatial cover age as the Zoom Window Classes are defined by drawing polygons and or vectors and
44. spectrum with any spectra already plotted The View Spectra Window provides various mechanisms for manipulating the spectra once they have been plotted The scale of the plot is automatically set to include all the spectra se lected each time a new spectrum is selected The user may also explicitly change the scale either by entering starting and ending wavelengths or reflectance values or by clicking on the appro priate axes SIPS_ View can either plot the spectra in the View Spectra Window overlaying one an other or stacked vertically offset from one an other Stacking the plot is useful for comparing spectra that have similar shapes and reflectance values Fig 5 SIPS_View can apply a fast Fourier transform filter to any plotted spectrum allowing smoothing of noisy data The FFT filter is used by grabbing the slider titled FFT Filter and changing the Spectral Imaging Processing System 155 value Upon releasing the slider the spectra are replotted with the filter applied The higher the value of the slider the fewer harmonics are used to draw the spectra and thus the smoother the spectra appear If there are very noisy bands in the data and these bands are not masked with a bad bands list then attempting to smooth the spectra using the FFT function may result in harmonic ringing Also filtering will only affect spectra with the same number of bands as the image data Color PostScript output can be produ
45. ta a case history from the north ern Grapevine Mountains Nevada California in Proceed ings 31st Annual International Technical Symposium So ciety of Photo Optical Instrumentation Engineers SPIE Bellingham WA Vol 834 pp 119 128 Kruse F A 1988 Use of Airborne Imaging Spectrometer data to map minerals associated with hydrothermally al tered rocks in the northern Grapevine Mountains Nevada and California Remote Sens Environ 24 1 31 51 Kruse F A and Dietz J B 1991 Integration of visible through microwave range multispectral image data sets for geologic mapping in Proceedings of the Cinqui me Colloque International Mesures Physiques et Signatures en T l d tection 14 18 January Courchevel France Euro pean Space Agency Paris France ESA SP 319 Vol 2 pp 481 486 Kruse F A Raines G L and Watson K 1985 Analytical techniques for extracting geologic information from multi channel airborne spectroradiometer and airborne imaging spectrometer data in Proceedings International Sympo sium on Remote Sensing of Environment Fourth Thematic Conference Remote Sensing for Exploration Geology San Francisco 1 4 April Environmental Research Institute of Michigan ERIM Ann Arbor MI pp 309 324 Kruse F A Kierein Young K S and Boardman J W 1990 Mineral mapping at Cuprite Nevada with a 63 channel imaging spectrometer Photogramm Eng Remote Sens 56 1 83 92 Kruse
46. tions by A T Shapiro P J Barloon and K B Heidebrecht J W Boardman wrote the SIPS spectral unmixing 162 Kruse et al routine and the Spectral Angle Mapper algorithm Continuing development and support of SIPS as a HIRIS team resource is funded by NASA Grant NAS5 30552 Dr Alexander F H Goetz Principal Investigator REFERENCES Boardman J W 1989 Inversion of imaging spectrometry data using singular value decomposition in Proceedings IGARSS 89 12th Canadian Symposium on Remote Sens ing Vol 4 IGARSS Canada pp 2069 2072 Available from the Institute of Electrical amp Electronics Engineers Piscataway NJ 08854 Boardman J W 1990 Inversion of high spectral resolution data in Proceedings SPIE Bellingham WA Vol 1298 pp 222 233 Boardman J W 1991 Sedimentary facies analysis using imaging spectrometry a geophysical inverse problem Ph D dissertation University of Colorado Boulder 212 p unpublished Boardman J W 1993a Spectral angle mapping a rapid measure of spectral similarity in preparation Boardman J W 1993b Sedimentary facies analysis using imaging spectrometry a geophysical inverse problem in preparation Center for the Study of Earth from Space CSES 1992 SIPS User s Guide The Spectral Image Processing System v 1 1 Boulder CO 74 pp Clark R N King T V V Klejwa M and Swayze G A 1990 High spectral resolution spectroscopy of

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