Overview of This Tutorial

This tutorial is designed to introduce you to advanced concepts and procedures for analysis of imaging spectrometer data or hyperspectral images . We will use 1995 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data from Cuprite, Nevada, to investigate the unique properties of hyperspectral data and how spectral information can be used to identify mineralogy. We will evaluate "Effort" "polished" spectra vs ATREM-calibrated data, and review the Spectral Angle Mapper classification. We will compare apparent reflectance spectra and continuum-removed spectra. We will also compare apparent reflectance images and continuum-removed images and evaluate Spectral Feature Fitting results. This tutorial is designed to be completed in two to four hours.

Files Used in This Tutorial

You must have the ENVI TUTORIALS & DATA CD-ROM mounted on your system to access the files used by this tutorial, or copy the files to your disk.

The files used in this tutorial are contained in the C95AVSUB subdirectory of the ENVIDATA directory on the ENVI TUTORIALS & DATA CD-ROM.

The files listed below, along with their associated .hdr files, are required to run this exercise. Optional files listed below may also be used if more detailed calibration comparisons are desired. All image data files have been converted to integer format by multiplying the reflectance values by 1000 because of disk space considerations. A value of 1000 therefore represents apparent reflectance of 1.0.

Required Files

CUP95_AT.INT	Cuprite ATREM calibrated apparent reflectance data. 50 bands (integer).

CUP95EFF.INT	Cuprite Effort-Corrected ATREM calibrated apparent reflectance data. 50 bands (integer).

JPL1.SLI	JPL Spectral Library in ENVI format.

USGS_MIN.SLI	USGS Spectral Library in ENVI format.

CUP95_AV.ROI	Saved ROI locations.

CUPSAMEM.ASC	Optional file of ROI mean spectra.

CUPSAM1.IMG	SAM classification image using ROI image spectra endmembers.

CUPRUL1.IMG	SAM rule image using ROI image spectra endmembers.

CUPSAM2.IMG	SAM classification image using Spectral Library endmembers.

CUPRUL2.IMG	SAM rule images using Spectral Library endmembers.

CUP95_CR.DAT	Continuum-removed data (floating point)

CUP95SFF.DAT	Spectral Feature Fitting Results

CUP95SFR.DAT	Spectral Feature Fitting Band-Math Results

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Removal of Residual Calibration Errors using "Effort"

"Effort", the "Empirical Flat Field Optimized Reflectance Transformation" is a correction used to remove residual "saw-tooth" instrument (or calibration-introduced) noise and atmospheric effects from ATREM-calibrated AVIRIS data. While this correction is not currently available in ENVI, it is the type of custom correction that might be designed for specific data sets to improve overall quality of spectra. It provides the best reflectance spectra available from AVIRIS data, so the Effort data are used in later exercises in this tutorial. Effort is a relatively automated improvement on the Flat-Field Calibration method (Boardman, 1996). The Effort correction selects those AVIRIS spectra that match a low order polynomial estimate in a least-squares sense as representative featureless spectra. These spectra are averaged and a mild gain factor is determined to remove systematic, coherent noise, present in every spectrum, including small residual atmospheric effects near 2.0 mm range attributable to CO2. We will compare 1995 AVIRIS data calibrated to reflectance using ATREM and the corresponding Effort-corrected ATREM data.

Open and Load the 1995 Effort-Corrected Data

1. Open the file CUP95EFF.INT by selecting File->Open Image File from the ENVI Main Menu .
2. This is the 1995 AVIRIS ATREM-calibrated apparent reflectance data with the Effort correction applied.
3. Start a new display by clicking on the "New" button in the Available Bands List.
4. Load band 193 (2.20 mm) as a grayscale image in the new image display by clicking "Load Band".
5. Start a Z-Profile by clicking the right mouse button in the Main Image display and selecting Functions->Profiles->Z-Profile.
6. Move the profile to the bottom of the screen for comparison with the 1995 ATREM data.

Compare ATREM and Effort Spectra

1. Click the right mouse button in the Effort Main Image display and select Functions->Link->Link Display.
2. Click "OK" in the Link Display dialog to activate the link.
3. Select Functions->Link->Dynamic Overlay Off to enable normal mouse interaction in the Effort Main Display window.

Moving the Zoom Window indicator box in this image will cause the cursor location to be updated in the ATREM Main Display window. Both spectral profiles will show the spectrum for the current pixel.

1. Move the Zoom Window indicator box around the image and compare the corresponding ATREM and Effort spectra. Note where the major differences occur.
2. Now position the cursor at 503, 581 by selecting Functions->Interactive Analysis->Pixel Locator in the Main Display window.
3. Compare the Effort-corrected spectrum to the ATREM-only spectrum
4. Drag and drop the Effort spectrum into the plot with the ATREM-only spectrum by clicking with the right mouse button to the right of the plot axis to display the name, grabbing the first character of the name and dragging with the left mouse button, and releasing in the ATREM-only plot window.
5. Change the color of the Effort spectrum by selecting Edit->Data Parameters" in the Plot Window menu bar.

Stack the spectra if desired by selecting Options->Stack Data.

Note how similar the two spectra are, but how very small coherent noise "wiggles" have been removed from the Effort spectrum. Also note the correction of residual CO2 at 2.01 mm.

1. Delete the imported spectrum in the ATREM spectral profile window by clicking to the right of the right plot axis to display the names, and clicking on the first character of the name using the right mouse button.

1. Now move the cursor location to 542, 533 using the Pixel Locator and repeat the drag-and-drop comparison as described above.

Note how Effort changes the spectrum.

1. Try selecting some other points in the two images using the Pixel Locator and compare the spectra.
2. What are the major differences?

Close All Files, Displays, and Plots

To close all of the files used in the previous section, select File->Close All Files.

The associated display windows will also be closed.

To close all of the spectral plots, select Basic Tools->Display Controls->Close All Plot Windows.

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Spectral Angle Mapper Classification

Next, we will use both image and laboratory spectra to classify the AVIRIS data using the "Spectral Angle Mapper" (SAM). We will go through the endmember selection process for SAM, but will not actually run the algorithm. We will examine previously calculated classification results to answer specific questions about the strengths and weaknesses of the SAM classification.

The Spectral Angle Mapper (SAM) is an automated method for comparing image spectra to individual spectra or a spectral library (Boardman, unpublished data; CSES, 1992; Kruse et al ., 1993a). SAM assumes that the data have been reduced to apparent reflectance (true reflectance multiplied by some unknown gain factor controlled by topography and shadows). The algorithm determines the similarity between two spectra by calculating the "spectral angle" between them, treating them as vectors in a space with dimensionality equal to the number of bands ( nb ). A simplified explanation of this can be given by considering a reference spectrum and an unknown spectrum from two-band data. The two different materials will be represented in the 2-D scatter plot by a point for each given illumination, or as a line (vector) for all possible illuminations

Because it uses only the "direction" of the spectra, and not their "length," the method is insensitive to the unknown gain factor, and all possible illuminations are treated equally. Poorly illuminated pixels will fall closer to the origin. The "color" of a material is defined by the direction of its unit vector. Notice that the angle between the vectors is the same regardless of the length. The length of the vector relates only to how fully the pixel is illuminated.

The SAM algorithm generalizes this geometric interpretation to nb -dimensional space. SAM determines the similarity of an unknown spectrum t to a reference spectrum r , by applying the following equation (CSES, 1992):

which also can be written as:

where nb equals the number of bands in the image.

For each reference spectrum chosen in the analysis of a hyperspectral image, the spectral angle a is determined for every image spectrum (pixel). This value, in radians, is assigned to the corresponding pixel in the output SAM image, one output image for each reference 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. Gray-level thresholding is typically used to empirically determine those areas that most closely match the reference spectrum while retaining spatial coherence.

The SAM algorithm implemented in ENVI takes as input a number of "training classes" or reference spectra from ASCII files, ROIs, or spectral libraries. It calculates the angular distance between each spectrum in the image and the reference spectra or "endmembers" in n -dimensions. The result is a classification image showing the best SAM match at each pixel and a "rule" image for each endmember showing the actual angular distance in radians between each spectrum in the image and the reference spectrum. Darker pixels in the rule images represent smaller spectral angles, and thus spectra that are more similar to the reference spectrum. The rule images can be used for subsequent classifications using different thresholds to decide which pixels are included in the SAM classification image.

Select Image Endmembers

1. Open the 1995 AVIRIS ATREM-calibrated apparent reflectance data file, CUP95EFF.INT .
2. Load band 193 (2.20 mm) as a grayscale image in the new image display by clicking "Load Band" in the Available Bands List.
3. Start the SAM endmember selection process by selecting Classification->Supervised Classification->Spectral Angle Mapper.

You may also make this selection from the Spectral Tools Menu.

1. Select the file CUP95EFF.INT as the input file and click "OK".
2. When the SAM Endmember Collection dialog appears, select Import->from ASCII File.

The Enter ASCII Plot Filename dialog will appear and available files will be listed.

1. Enter "*.asc" in the File Name text box to list all of the ASCII files available.
2. Select the file CUP95_EM.ASC and click "OK" to open the file.
3. When the Input ASCII File dialog appears, deselect spectra 3, 4, 9, and 11 by clicking on the checked boxes to the left of the number spectra.

This will leave you with the mean spectra Zeolite, Calcite, Alunite (2.16), Kaolinite, Illite/Muscovite, Silica (Bright), and Buddingtonite.

1. Click "OK" to load all of the endmember spectra into the SAM Endmember Collection dialog.
2. Plot the endmember spectra by clicking "Plot Endmembers".
3. Stack the spectra for improved comparison of spectral features (Figure 3) by selecting Options->Stack Data in the plot window.

Normally, you would click "OK" to start the classification, but because classification can take some time, preprocessed results are provided for this exercise.

1. Click "Cancel" and continue by reviewing previously calculated SAM results as described below.

Execute SAM, Resources Permitting

1. If sufficient time and resources are available, click "Apply" and enter output file names in the Spectral Angle Mapper Parameters dialog and click "OK".
2. Open the SAM classification image by selecting File->Open Image File and choosing CUPSAM1.IMG as the input file name.

The classification image is one band with coded values for each class (for example, alunite is coded as "1").

When opened, the classified image will appear in the Available Bands List dialog.

1. Ensure that the "Grayscale" toggle button is selected.
2. Load the SAM classification image into a new ENVI display window by clicking on the classified image name, clicking "New" and then "Load".

The classes will automatically be color coded as follows:

Mineral	Color
Zeolites	White
Calcite	Green
Alunite	Yellow
Kaolinite	Red
Illite/Muscovite	Dark Green
Silica	Blue
Buddingtonite	Maroon

• The number of pixels displayed as a specific class is a function of the threshold used to generate the classification. Just because a given pixel is classified as a specific mineral doesn't make it so. SAM is a similarity measure, not an identifier.
• Open the Effort data by selecting File->Open Image File and choosing the file CUP95EFF.INT.
• Click on "RGB" Color in the Available Bands List, open a new display by clicking on "New", and load bands 183, 193, and 207 as an RGB image.
• Start a spectral profile by selecting Functions->Profiles->Z-Profile in the Main Image display.
• Compare the SAM classification results with the distributions shown by the color composite image.
• Compare the actual spectra for classified pixels with the endmember spectra using spectral browsing.

Use image linking for direct comparison, if desired.

1. Load the SAM rule images for this classification.
2. Open the SAM rule image by selecting File->Open Image File and choosing CUPRUL1.IMG as the input file name.

The rule image has one band for each endmember classified, with the pixel values representing the spectral angle in radians. Lower spectral angles (darker pixels) represent better spectral matches to the endmember spectrum.

When opened, one band for each endmember will appear in the Available Bands List dialog.

1. Ensure that the "Grayscale" toggle button is selected and load a SAM rule image into a third ENVI display window by clicking on the classified image name, clicking "New" and then "Load".
2. Evaluate the image with respect to the color composite and the SAM classification image as well as the ROI means and individual spectra extracted using the Z Profiler.
3. Select Functions->Color Mapping->ENVI Color Tables in the Main Image.
4. Use the "Stretch Bottom" and "Stretch Top" sliders to adjust the SAM rule thresholds to highlight those pixels with the greatest similarity to the selected endmember.
5. Pull the Stretch Bottom slider all the way to the right and the Stretch Top slider all the way to the left to highlight the most similar pixels in white.
6. Move the Stretch Bottom slider gradually to the left to reduce the number of highlighted pixels and show only the best SAM matches in white.

You may use a rule image color composites or image animation if desired to compare individual rule images.

1. Repeat the process with each SAM rule image.
2. Click "Cancel" when finished to close the ENVI Color Tables dialog.

Select Spectral Library Endmembers

In this exercise we will once again go through the endmember selection process, but we won't actually perform the SAM classification. Previously saved SAM results will be used for comparisons. If you have time, you can perform your own SAM classification using spectral library endmembers.

1. Open the spectral library if not previously loaded.
2. Select Spectral Tools->Spectral Libraries->Spectral Library Viewer.
3. Click "Open New File", select the file JPL1SLI.DAT, and click "OK".
4. Use the Spectral Library Viewer to plot the following spectra in the Spectral Library Viewer window by clicking on the appropriate mineral name in the list of spectra.

 

• ALUNITE SO-4A
• BUDDINGTONITE FELDS TS-11A
• CALCITE C-3D
• CHABAZITE TS-15A (a zeolite mineral)
• ILLITE PS-11A
• KAOLINITE WELL ORDERED PS-1A

 

1. Start the SAM endmember selection process by selecting Classification->Supervised Classification->Spectral Angle Mapper.,

You may also access this function from the ENVI Spectral Tools menu.

1. Select the file CUP95EFF.INT as the input file and click "OK".
2. When the SAM Endmember Collection dialog appears, select Import Spectra->from Spectral Library.
3. Select the file JPLSLI1.DAT and click "OK".
4. If you haven't already done so using the Spectral Library Viewer, select the following spectra for the classification from the JPL Spectral Library by clicking on their names in the Spectral Library Input dialog and clicking "OK":

 

• ALUNITE SO-4A
• BUDDINGTONITE FELDS TS-11A
• CALCITE C-3D
• CHABAZITE TS-15A (a zeolite mineral)
• ILLITE PS-11A
• KAOLINITE WELL ORDERED PS-1A

 

1. Open the USGS spectral library.
2. Select Spectral Tools->Spectral Libraries->Spectral Library Viewer.
3. Click "Open New File", select the file USGS_SLI.DAT and click "OK".
4. Select the endmember "opal2.spc Opal TM8896 (Hyalite)" from the list of spectra by clicking on the name followed by clicking "OK".

Note that the spectra are automatically resampled to the AVIRIS wavelengths and resolution using the AVIRIS band positions and FWHM and listed in the Endmember Collection dialog.

1. Click "Plot Endmembers" to plot the endmember spectra for these AVIRIS data.
2. Choose Options->Stack Data in the plot window to offset the spectra vertically for comparison.
3. Compare these spectra to the image spectra plotted in the previous SAM exercise.
4. Since we will not actually be performing the SAM classification because of time constraints, click "Cancel" in the Endmember Collection dialog to continue the exercise.

If you have time, you can generate your own SAM classification by clicking "OK".

Review SAM Results

1. Open the pre-calculated SAM classification image generated using Spectral Library Endmembers by selecting File->Open Image File.
2. Select CUPSAM2.IMG as the input file name and click "OK".

The classification image is one band with coded values for each class (for example, alunite is coded as "1"). When opened, the classified image will appear in the Available Bands List dialog.

1. Ensure that the "Grayscale" toggle button is selected.
2. Load the SAM classification image into a new ENVI display window by clicking on the classified image name, clicking "New" and then "Load".

The classes will automatically be color coded as follows:

Mineral	Color
Zeolites	White
Calcite	Green
Alunite	Yellow
Kaolinite	Red
Illite/Muscovite	Dark Green
Silica	Blue
Buddingtonite	Maroon

• The number of pixels displayed as a specific class is a function of the threshold used to generate the classification. Just because a given pixel is classified as a specific mineral doesn't make it so. SAM is a similarity measure, not an identifier).
• Compare the classification results with the distributions shown by the color composite image, the previous classification using image spectra, and the library spectra.

Use image linking for direct comparison if desired.

1. Load the SAM rule images by selecting File->Open Image File and selecting CUPRUL2.IMG as the input file name and clicking "OK".

Again, the rule image has one band for each endmember classified, with the pixel values representing the spectral angle in radians. Lower spectral angles (darker pixels) represent better spectral matches to the endmember spectrum. When opened, one band for each endmember will appear in the Available Bands List dialog.

1. Ensure that the "Grayscale" toggle button is selected.
2. Load a SAM rule image into a new ENVI display window by clicking on the classified image name, clicking "New" and then "Load".
3. Evaluate the image using the ENVI Color Tables as above and with respect to the color composite and the SAM classification image as well as the ROI means and individual spectra extracted using the Z Profiler.

Also use a rule image color composite or image animation if desired to compare individual rule images.

1. Repeat the process with each SAM rule image.

Based upon the results of the two SAM classifications, answer the following questions:

1. What ambiguities exist in the SAM classification based on your images and spectra above?
2. Why are the two classifications so different? What factors could affect how well SAM matches the endmember spectra?
3. How could you determine which thresholds represent a true map of the selected endmembers?
4. Can you see the topographic shading effects in the SAM data? Why or why not?
5. Make a sketch map of the Cuprite surface mineralogy for all classes on a separate piece of paper. Do some classes co-occur?
6. In light of some of the ambiguities in the SAM classification, how could you select better endmembers?

Optional: Generate new SAM Classified Images Using Rule Classifier

If you have extra time at the end of the exercise, try generating new classified images based on different thresholds in the rule images.

1. Display the individual bands of one of the two previously calculated rule images CUPRUL1.IMG or CUPRUL2.IMG and define the threshold for the classification by browsing using the Cursor Location/Value dialog.

Thresholds can also be defined using ENVI's interactive density slice tool, by selecting Functions->Color Mapping->Density Slice in the Main window.

1. Now select Functions->Classification->Rule Classifier rule file as viewed above for classification.

The Rule Image Classifier Parameters dialog will appear.

1. Select "classify by minimum" by clicking on the toggle arrow in the Rule Image Classifier Parameters dialog and enter the previously defined SAM threshold.

All of the pixels with values lower than the minimum will be classified. Lower thresholds result in fewer pixels being classified.

1. Select either "Memory" or "File" processing and click "OK" to begin the processing.

After a short wait, the new classification image will be listed in the Available Bands List.

1. Click on the classified image name and then "Load" to load the image into an ENVI display window.
2. Compare with previous classifications and comment on the differences and what they mean.

Close Files and Plots

To close all of the files used in this portion of the exercise, select File->Close All Files.

To close all of the spectral plots, select Basic Tools->Display Controls->Close All Plot Windows.

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Spectral Feature Fitting (SFF) and Analysis

Spectral Feature Fitting (SFF) is an absorption-feature-based method for matching image spectra to reference endmembers, similar to methods developed at the U. S. Geological Survey, Denver (Clark et al., 1990, 1991, 1992; Clark and Swayze, 1995).

Most methods used for analysis of hyperspectral data still do not directly identify specific materials. They only indicate how similar the material is to another known material or how unique it is with respect to other materials. Techniques for direct identification of materials, however, via extraction of specific spectral features from field and laboratory reflectance spectra have been in use for many years (Green and Craig, 1985; Kruse et al., 1985; Yamaguchi and Lyon, 1986; Clark et al., 1987). Recently, these techniques have been applied to imaging spectrometer data, primarily for geologic applications (Kruse et al., 1988; Kruse, 1988; Kruse, 1990; Clark et al., 1990, 1991, 1992; Clark and Crowley, 1992; Kruse et al. 1993b, 1993c; Kruse and Lefkoff, 1993, Swayze et al., 1995).

All of these methods require that data be reduced to reflectance and that a continuum be removed from the reflectance data prior to analysis. A continuum is a mathematical function used to isolate a particular absorption feature for analysis (Clark and Roush, 1984; Kruse et al, 1985; Green and Craig, 1985). It corresponds to a background signal unrelated to specific absorption features of interest. Spectra are normalized to a common reference using a continuum formed by defining high points of the spectrum (local maxima) and fitting straight line segments between these points. The continuum is removed by dividing it into the original spectrum (Figure 4).

Spectral feature fitting requires that reference endmembers be selected from either the image or a spectral library, that both the reference and unknown spectra have the continuum removed, and that each reference endmember spectrum be scaled to match the unknown spectrum. A "Scale" image is produced for each endmember selected for analysis by first subtracting the continuum-removed spectra from one, thus inverting them and making the continuum zero. A single multiplicative scaling factor is then determined that makes the reference spectrum match the unknown spectrum. Assuming that a reasonable spectral range has been selected, a large scaling factor is equivalent to a deep spectral feature, while a small scaling factor indicates a weak spectral feature.

.A least-squares-fit is then calculated band-by-band between each reference endmember and the unknown spectrum. The total root-mean-square (RMS) error is used to form an RMS image for each endmember. An optional ratio image of Scale/RMS provides a "Fit" image that is a measure of how well the unknown spectrum matches the reference spectrum on a pixel-by-pixel basis.

Open and Load the Continuum-Removed Data

For the purposes of this exercise, you will not create your own continuum-removed data, they have been pre-calculated.

If you want to perform this operation on your own:

1. Open the file CUP95EFF.INT and select Spectral Tools->Continuum Removal.
2. Select the CUP95EFF.INT file, perform spectral subsetting if desired to limit the spectral range for continuum removal, and click "OK".
3. Enter the continuum-removed output file name in the Continuum Removal Parameters dialog and click "OK" to create the continuum-removed image.

This image will have the same number of spectral bands as the input image.

If not creating your own continuum-removed file:

1. Open the file CUP95_CR.DAT .

This is the continuum-removed data (floating point 0 - 1.0 range) derived from the 1995 AVIRIS Effort-calibrated apparent reflectance as described above.

1. Load band 193 (2.20 mm) as a grayscale image by clicking "Load Band" in the Available Bands List.
2. Start a Z-Profile by clicking the right mouse button in the Main Image display and selecting Functions->Profiles->Z-Profile.
3. Select Options->Auto-scale Y-axis Off in the Spectral Profile window.
4. Choose Edit->Plot Parameters in the Spectral Profile window, click on the "Y-Axis" radio button, and enter the range 0.5 - 1.0 in the appropriate text boxes for the Y-axis.
5. Either press enter after entering the values, or click "Apply" at the bottom of the dialog to apply the new Y-axis range.
6. Click "Cancel" to close the Plot Parameters dialog.
7. Move the Spectral Profile window to the bottom of the screen for comparison with the Effort data.

Open and Load the 1995 Effort-Corrected Data

1. Open the file CUP95EFF.INT .

This is the 1995 AVIRIS ATREM-calibrated apparent reflectance data with the Effort correction applied.

1. Start a new display by clicking "New" in the Available Bands List.
2. Load band 193 (2.20 mm) as a grayscale image in the new image display by clicking "Load Band".
3. Start a Z-Profile by clicking the right mouse button in the Main Image display and selecting Functions->Profiles->Z-Profile.
4. Select Options->Auto-scale Y-axis Off in the Spectral Profile window.
5. Choose Edit->Plot Parameters in the Spectral Profile window, click on the "Y-Axis" radio button, and enter the range 0 - 500 in the appropriate text boxes for the Y-axis.
6. Either press enter after entering the values, or click "Apply" at the bottom of the dialog to apply the new Y-axis range.
7. Click "Cancel" to close the Plot Parameters dialog.
8. Move the Spectral Profile window to the bottom of the screen for comparison with the continuum-removed data.

Compare Continuum-removed spectra and Effort Spectra

1. Click the right mouse button in the Continuum-Removed Main Image display and select Functions->Link->Link Display.
2. Click "OK" in the Link Display dialog to activate the link.
3. Now select Functions->Link->Dynamic Overlay Off to enable normal mouse interaction in the Continuum-Removed Main display.

Moving the Zoom Window indicator box in this image will cause the cursor location to be updated in the Effort Main Display window. Both spectral profiles will show the spectrum for the current pixel.

1. Move the Zoom window indicator box around the image and compare the corresponding Continuum-Removed and Effort spectra.

Note how the continuum-removed spectrum normalizes and enhances spectral features.

1. Now position the cursor at 503, 581 by selecting Functions->Interactive Analysis->Pixel Locator in the Main display and compare the continuum-removed spectrum to the Effort spectrum

1. Move the cursor location to 542, 533 using the Pixel Locator and repeat the comparison.

Note how continuum-removal affects the spectrum.

1. Try selecting some other points in the two images using the Pixel Locator and compare the spectra.
2. What are the major differences? What improvements in visual analysis does the continuum-removal procedure allow?

Compare Continuum-Removed and Effort Images

1. Load a color composite image consisting of Effort bands 183, 193, and 207 (2.10, 2.20, and 2.34 mm) into RGB.
2. Click the left mouse button in the Effort Main Image display to activate the dynamic overlay.
3. Compare the continuum-removed image for band 193 (2.20 mm) to the color composite image.

Note the correspondence between dark areas on the continuum-removed image and red-to-purple areas on the color composite. The dark areas are areas with absorption bands near 2.2 mm.

1. Move the Zoom window indicator box to some of these dark areas by dragging with the left mouse button or by clicking in the desired location with the middle mouse button.
2. Compare the corresponding spectral profiles for both the continuum-removed data and the Effort data and note the image colors.
3. Load continuum-removed band 207 (2.34 mm).

Note by moving the Zoom window to the dark areas and examining the spectra that these correspond to absorption features near 2.34 mm in both the continuum-removed and Effort spectra.

Close the Effort Display and Spectral Profile

Click on one of the Effort bands in the Available Bands List and then click "Close File" at the bottom of the dialog.

The associated display windows and spectral plot will also be closed.

Open and Load the Spectral Feature Fitting Scale and RMS Images

For the purposes of this exercise, you will not create your own SFF Scale or RMS data, they have been pre-calculated.

If you want to perform this operation on your own:

1. Open the file CUP95_CR.DAT and select Spectral Tools->Mapping Methods->Spectral Feature Fitting.
2. Select the CUP95_CR.DAT file, perform spectral subsetting if desired to limit the spectral range for fitting, and click "OK".
3. Use ENVI's standardized Endmember Collection dialog to import image or library spectra to use as endmembers in the SFF and click "Apply" in the Endmember Collection dialog.
4. Choose "Output separate Scale and RMS Images" in the Spectral Feature Fitting Parameters dialog, enter an output file name, and click "OK" to create the Scale and RMS images.

The output image will have two images for each endmember, a Scale image and an RMS image.

If you are not going to create your own SFF file:

1. Open the file CUP95SFF.DAT .

This contains the Scale and RMS images derived from spectral feature fitting of a library of image endmember spectra from the 1995 AVIRIS Effort-calibrated apparent reflectance data. Library spectra could also have been used, but the image spectra were used to allow direct comparison with other methods that use image spectra.

1. Load the "Scale (Mean: Kaolinite...)" image into a new display as a grayscale image by clicking "New", followed by the band name and "Load Band" in the Available Bands List.
2. Load the "Scale (Mean: Alunite 2.16...)" image into a new display as a grayscale image by again clicking "New", the band name, and "Load".
3. Link the two Scale image displays and the Continuum-Removed display by clicking the right mouse button in one of the displays and selecting Functions->Link->Link Displays.
4. In the Link Displays dialog, click "OK".
5. Use the dynamic overlay function to compare the images.
6. Select Basic Tools->Cursor Location/Value, move the cursor around one of the images, and compare the actual values for the two Scale images.

Note that although similar as stretched images, the values for the two images are very different.

1. Load the "RMS (Mean: Kaolinite...)" image into the display that contains the Kaolinite Scale Image and "RMS (Mean: Alunite 2.16...)" image into the display that contains the Alunite Scale Image as grayscale images by clicking on the appropriate band name, and clicking "Load".
2. Again, use the dynamic overlay function to compare the images and the Cursor Location/Value functions to compare the actual values for the two RMS images.

Low RMS values correspond to good spectral matches.

2-D Scatterplots of SFF Results

1. Start 2-D Scatterplots from the RMS (Mean: Kaolinite...) image by clicking the right mouse button in the Main Image display and selecting Functions->Interactive Analysis->2-D Scatterplots.
2. Choose the Kaolinite Scale and RMS images for Bands X and Y.
3. Draw a Region of Interest (ROI) on the scatterplot at low RMS values for all ranges of Scale by clicking the left mouse button to draw lines connecting the vertices of a polygon and the right mouse button to close the polygon.

Note the highlighted pixels in the RMS Kaolinite.

1. Select the Options->Change Bands in the Scatterplot Window.
2. Load other combinations of Scale and RMS for the various endmembers.
3. Close the 2-D scatterplot by selecting File->Cancel in the Scatterplot window.
4. Select Basic Tools->Display Controls->Close All Plot Windows.

Spectral Feature Fitting Ratios - "Fit" Images

For the purposes of this exercise, you will not create your own SFF Fit Images, they have been pre-calculated.

If you want to perform this operation on your own:

1. Open the file CUP95_CR.DAT and select Spectral Tools->Mapping Methods->Spectral Feature Fitting.
2. Select the CUP95_CR.DAT file, perform spectral subsetting if (desired) to limit the spectral range for fitting, and click "OK".
3. Use ENVI's standardized Endmember Collection dialog to import image or library spectra to use as endmembers in the SFF and click "Apply" in the Endmember Collection dialog.
4. Use the toggle arrows in the Spectral Feature Fitting Parameters dialog to select "Output combined (Scale/RMS) images, enter an output file name, and click "OK" to create the Fit images.

The output image will have one Fit image for each endmember.

If not creating your own SFF file:

1. Open the file CUP95SFR.DAT .

This contains the images that are the ratio of Scale/RMS for each of the endmembers.

1. Load the "Fit (Mean: Kaolinite...)" image into the existing display containing the continuum-removed data as a grayscale image by entering the appropriate display number in the "Active Display" text box in the Available Bands List.
2. Click on the band name and click "Load Band".

The bright pixels represent the best fit to the reference endmember spectrum of Kaolinite.

1. Load the "Scale (Mean: Kaolinite...)" and "RMS (Mean: Kaolinite...)" images into the other two displays if not already loaded.
2. Link the image displays and compare the Fit, Scale, and RMS images.

Note how the Scale and RMS image interact to produce the Fit result.

1. Load the Effort data again and extract spectral profiles for the bright pixels in the Fit image.
2. Compare the Fit, Scale, and RMS images as well as the spectral profiles for the other endmembers.
3. Make one or more color composite images by loading Fit images for different endmembers as RGB.
4. What conclusions can you draw regarding the effectiveness of spectral feature fitting for identifying specific endmembers?

Close Files and Exit ENVI.

When you have finished your ENVI session, click "Quit" or "Exit" on the ENVI Main Menu, then type exit at the IDL command prompt.

If you are using ENVI RT, quitting ENVI will take you back to your operating system.

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References

Clark, R. N., and Roush, T. L., 1984, Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications: Journal of Geophysical Research, v. 89, no. B7, pp. 6329-6340.

Clark, R. N., King, T. V. V., and Gorelick, N. S., 1987, Automatic continuum analysis of reflectance spectra: in Proceedings, Third AIS workshop, 2-4 June, 1987, JPL Publication 87-30, Jet Propulsion Laboratory, Pasadena, California, p. 138-142.

Clark, R. N., Swayze, G. A., Gallagher, A., King, T. V. V., and Calvin, W. M., 1993, The U. S. Geological Survey Digital Spectral Library: Version 1: 0.2 to 3.0 mm: U. S. Geological Survey, Open File Report 93-592, 1340 p.

Clark, R. N., Gallagher, A. J., and Swayze, G. A., 1990, Material absorption band depth mapping of imaging spectrometer data using the complete band shape least-squares algorithm simultaneously fit to multiple spectral features from multiple materials: in Proceedings of the Third Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, JPL Publication 90-54, p. 176 - 186.

Clark, R. N., Swayze, G. A., Gallagher, A., Gorelick, N., and Kruse, F. A., 1991, Mapping with imaging spectrometer data using the complete band shape least-squares algorithm simultaneously fit to multiple spectral features from multiple materials: in Proceedings, 3rd Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, JPL Publication 91-28, p. 2-3.

Clark, R. N., Swayze, G. A., and Gallagher, A., 1992, Mapping the mineralogy and lithology of Canyonlands, Utah with imaging spectrometer data and the multiple spectral feature mapping algorithm: in Summaries of the Third Annual JPL Airborne Geoscience Workshop, JPL Publication 92-14, v 1, p. 11-13.

Center for the Study of Earth from Space (CSES), 1992, SIPS User's Guide, The Spectral Image Processing System, v. 1.1, University of Colorado, Boulder,74 p.

Crowley, J. K., and Clark, R. N., 1992, AVIRIS study of Death Valley evaporite deposits using least-squares band-fitting methods: in Summaries of the Third Annual JPL Airborne Geoscience Workshop, JPL Publication 92-14, v 1, p. 29-31.

Clark, R. N., and Swayze, G. A., 1995, Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice, and snow, and other materials: The USGS Tricorder Algorithm: in Summaries of the Fifth Annual JPL Airborne Earth Science Workshop, JPL Publication 95-1, p. 39 - 40.

Green, A. A., and Craig, M. D., 1985, Analysis of aircraft spectrometer data with logarithmic residuals: in Proceedings, AIS workshop, 8-10 April, 1985, JPL Publication 85-41, Jet Propulsion Laboratory, Pasadena, California, p. 111-119.

Kruse, F. A., Raines, G. L., and Watson, K., 1985, Analytical techniques for extracting geologic information from multichannel airborne spectroradiometer and airborne imaging spectrometer data: in Proceedings, International Symposium on Remote Sensing of Environment, Thematic Conference on Remote Sensing for Exploration Geology, 4th, Environmental Research Institute of Michigan, Ann Arbor, p. 309-324.

Kruse, F. A., Calvin, W. M., and Seznec, O., 1988, Automated extraction of absorption features from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Geophysical Environmental Research imaging spectrometer (GERIS) data: In Proceedings of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) performance evaluation workshop, JPL Publication 88-38, p. 62-75.

Kruse, F. A., 1988, Use of Airborne Imaging Spectrometer data to map minerals associated with hydrothermally altered rocks in the northern Grapevine Mountains, Nevada and California: Remote Sensing of Environment, v. 24, no. 1, pp. 31-51.

Kruse, F. A., 1990, Artificial Intelligence for Analysis of Imaging Spectrometer Data: Proceedings, ISPRS Commission VII, Working Group 2: "Analysis of High Spectral Resolution Imaging Data", Victoria, B. C., Canada, 17-21 September, 1990, p. 59-68.

Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, J. P., and Goetz, A. F. H., 1993a, The spectral image processing system (SIPS) - Interactive visualization and analysis of imaging spectrometer data: Remote Sensing of Environment, v. 44, p. 145 - 163.

Kruse, F. A., and Lefkoff, A. B., 1993b, Knowledge-based geologic mapping with imaging spectrometers: Remote Sensing Reviews, Special Issue on NASA Innovative Research Program (IRP) results, v. 8, p. 3 - 28.

Kruse, F. A., Lefkoff, A. B., and Dietz, J. B., 1993c, Expert System-Based Mineral Mapping in northern Death Valley, California/Nevada using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS): Remote Sensing of Environment, Special issue on AVIRIS, May-June 1993, v. 44, p. 309 - 336.

Swayze, g. A., and Clark, R. N., 1995, Spectral identification of minerals using imaging spectrometry data: evaluating the effects of signal to noise and spectral resolution using the Tricorder Algorithm: in Summaries of the Fifth Annual JPL airborne Earth Science Workshop, JPL Publication 95-1, p. 157 - 158.

Yamaguchi, Yasushi, and Lyon, R. J. P., 1986, Identification of clay minerals by feature coding of near-infrared spectra: in Proceedings, International Symposium on Remote Sensing of Environment, Fifth Thematic Conference, "Remote Sensing for Exploration Geology", Reno, Nevada, 29 September- 2 October, 1986, Environmental Research Institute of Michigan, Ann Arbor, p. 627-636.

Selected Mapping Methods Using Hyperspectral Data