Overview of This
Tutorial
Background: Imaging Spectrometry
Introduction to Basic
ENVI Functionality
Working With Cuprite Radiance
Data
Comparison of
Radiance and ATREM Reflectance
Compare Different Calibrations
References
This tutorial is designed to introduce you to the concepts of Imaging Spectrometry , hyperspectral images , and selected spectral processing basics using ENVI. For this exercise, we will use Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data to familiarize you with spatial and spectral browsing of imaging spectrometer data. We will start with 1995 AVIRIS radiance data for Cuprite, Nevada, USA, provided by Jet Propulsion Laboratory (JPL) and then compare the results of several reflectance calibration procedures. This tutorial is designed to be completed in two to four hours.
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.
CUP95_RD.INT Cuprite AVIRIS radiance data. 400 samples x 350 lines x 50 bands (integer). CUP95_AT.INT Cuprite ATREM-calibrated apparent reflectance data. 50 bands (integer). CUP95CAL.SLI Spectral Library of calibration results for selected minerals (integer). JPL1.SLI JPL Spectral Library in ENVI format. USGS_MIN.SLI USGS Spectral Library in ENVI format.
CUP95_FF.INT Cuprite Flat-Field-calibrated apparent reflectance data. 50 bands (Integer). CUP95_IA.INT Cuprite Internal Average Relative Reflectance (IARR) data. 50 bands (Integer). CUP95_EL.INT Cuprite Empirical Line calibrated apparent reflectance data. 50 bands (Integer).
Imaging spectrometers or "hyperspectral sensors" are remote sensing instruments that combine the spatial presentation of an imaging sensor with the analytical capabilities of a spectrometer. They may have up to several hundred narrow spectral bands with spectral resolution on the order of 10 nm or narrower (Goetz et al ., 1985). Imaging Spectrometers produce a complete spectrum for every pixel of the image (Figure 1). Compare this to broad-band multispectral scanners such as Landsat Thematic Mapper (TM), which only has 6 spectral bands and spectral resolution on the order of 100 nm or greate (Figure 2). The end result of the high spectral resolution of imaging spectrometers is that we can identify materials, where with broad-band sensors we could previously only discriminate between materials.
Figure 1: The imaging spectrometer concept; hundreds of spectral images, thousands to millions of individual spectra (from Vane, 1985).
Figure 2. Comparison of a simulated Landsat TM spectrum to the corresponding laboratory spectrum.
This portion of the tutorial is designed to familiarize you with ENVI features that are useful for spectral processing of imaging spectrometer data.
Before attempting to start the program, ensure that ENVI is properly installed as described in the installation guide.
The ENVI Main Menu appears when the program has successfully loaded and executed.
The file contains 50 bands (1.99 - 2.48 mm) of JPL-calibrated AVIRIS radiance for the Cuprite Mining District, Nevada, USA.
The Available Bands List dialog will appear, listing the 50 spectral band names.
An ENVI image display containing the selected band will appear.
The zoom region will be automatically updated.
The color image will be loaded into the new (second) image display.
Images are "linked" to allow simultaneous, identical user action on multiple images. Once linked, moving the zoom box, the scroll box, changing the zoom factor, or resizing any of the image windows causes the same actions to occur in the linked windows.
The Link Displays dialog will appear (Figure 2). 
Note how the Display #2 Zoom window updates to correspond with the first display.
"Multiple Dynamic Overlays" are available when two or more images are linked, allowing real-time overlay and toggling (flicker) of multiple grayscale or color images. Dynamic overlays are activated automatically when two or more windows are first linked.
ENVI's "Z" profile capabilities provide integrated spectral analysis. You can extract spectra from any multispectral data set including MSS, TM, and higher spectral dimension data such as GEOSCAN (24 bands), GERIS (63 bands), and AVIRIS (224 bands).
The spectrum for the current cursor location will be plotted in a plot window. A vertical line on the plot is used to mark the wavelength position of the currently displayed band. If a color composite image is displayed, three colored lines will appear, one for each displayed band in the band's respective color (red, green, or blue).
The spectrum will be extracted and plotted for the new location.
The spectrum will be updated as the Zoom indicator box moves. Note that the spectra you are viewing are radiance--not reflectance--spectra, as you are currently working with Cuprite radiance data.
Select Options->Collect Spectra in the Spectral Profile Window to accumulate spectra in this plot (Figure 4). Select Options->Replace Spectrum to return to the standard spectral browsing mode.
Optionally, to collect spectra in another plot window, open a new plot window and save image spectra from the Spectral Profile Window.
The spectrum name will be displayed to the right of the plot.
The spectra will be offset vertically to allow interpretation.
Each spectrum is listed by name/location in the Data Parameters dialog.
You can "animate" grayscale images to make the spatial occurrence of spectral differences more obvious.
The Animation Input Parameters dialog will appear, listing the bands
(Figure 5). 
The Animation window and the Animation Controls dialog will appear. The selected bands are loaded individually into the Animation widow. A status bar appears as each image is processed.
Once all of the selected images have been loaded, the animation will start automatically. Selected bands are displayed sequentially.
The Animation Controls (Figure 6) are used to specify the animation characteristics.
Continue this exercise using the images displayed in the first section.
The color image will be loaded into the current image display.
To extract selected image radiance spectra for specific targets in the AVIRIS radiance data.
| Location Name | Sample (with offset) |
Line (with offset) |
|---|---|---|
| Stonewall Playa | 590 | 570 |
| Varnished Tuff | 435 | 555 |
| Silica Cap | 494 | 514 |
| Opalite Zone with Alunite | 531 | 541 |
| Strongly Argillized Zone with Kaolinite | 502 | 589 |
| Buddingtonite Zone | 448 | 505 |
| Calcite | 260 | 613 |
Note how similar the radiance spectra appear. The overall shape of the spectra is caused by the typical combined solar/atmospheric response.
Note small absorption features (minima) near 2.2 micrometers that may be attributable to surface mineralogy.
Now compare apparent reflectance spectra from the image to selected library reflectance spectra.
JPL1.SLI will appear in the "Select Input File" field of the dialog.
This will allow direct visual comparison of radiance (Figure 7) and reflectance (Figure 9), though the Y-axes will not have the same scale.
Note how difficult it is to visually identify the minerals by comparing features in the radiance spectra to absorption features shown in the laboratory spectra.
Note the effect of the superimposed convex-upward solar-atmospheric signature in the AVIRIS radiance data on visual identification.
In this portion of the tutorial you will extract selected image radiance spectra and compare them to ATREM apparent reflectance spectra for specific targets in the AVIRIS radiance data.
The ATmospheric REMoval Program (ATREM) is a radiative transfer model-based technique for deriving scaled surface reflectance from AVIRIS data without a priori knowledge of surface characteristics (Gao and Goetz, 1990, CSES, 1992). It utilizes the 0.94 and 1.1 micrometer water vapor bands to calculate water vapor on a pixel-by-pixel basis from the AVIRIS data, the solar irradiance curve above the atmosphere, and transmittance spectra for each of the atmospheric gases CO2, O3, N2O, CO, CH4, and O2. At the time this tutorial was released, ATREM ran only on UNIX hardware. It can be obtained via anonymous ftp from cses.colorado.edu or by contacting the Center for the Study of Earth from Space at 303-492-5086. The ATREM-calibrated data used for this tutorial were reduced to apparent reflectance using ATREM 1.3.1.
Continue this exercise using the images displayed in the first section.
The color image will be loaded into the current image display.
Now open a second AVIRIS data set.
This is 50 bands (1.99 - 2.48 mm) of AVIRIS data calibrated to apparent reflectance using the atmospheric model "ATREM" to process the AVIRIS radiance data. The 50 band names will be added to the Available Bands List.
The Z profiles for both images will change to show the radiance and apparent reflectance spectra at the current location.
Visually compare both radiance and apparent reflectance spectrum for this location using the two Z-Profiles.
| Location Name | Sample (with offset) |
Line (with offset) |
|---|---|---|
| Stonewall Playa | 590 | 570 |
| Varnished Tuff | 435 | 555 |
| Silica Cap | 494 | 514 |
| Opalite Zone with Alunite | 531 | 541 |
| Strongly Argillized Zone with Kaolinite | 502 | 589 |
| Buddingtonite Zone | 448 | 505 |
| Calcite | 260 | 613 |
Note: an alternate method for getting linked spectral profiles simultaneously from two or more images is to select Functions>Additional Z Profile and choose additional datasets for extraction of profiles.
This section of the tutorial compares several image apparent reflectance spectra. You will use a spectral library of apparent reflectance spectra generated using ENVI's "Flat Field Calibration," "Internal Average Relative Reflectance (IARR) Calibration," and "Empirical Line Calibration" functions to compare the characteristics of the various calibration methodologies. The calibration techniques used are briefly described below.
The "Flat Field Calibration" technique is used to normalize images to an area of known "flat" reflectance (Goetz and Srivastava, 1985; Roberts et al. , 1986). The method requires that you locate a large, spectrally flat, spectrally uniform area in the AVIRIS data, usually defined as a Region of Interest (ROI). The radiance spectrum from this area is assumed to be composed of primarily atmospheric effects and the solar spectrum. The average AVIRIS radiance spectrum from the ROI is used as the reference spectrum, which is then divided into the spectrum at each pixel of the image. The result is apparent reflectance data that can be compared with laboratory spectra.
The IARR calibration technique is used to normalize images to a scene average spectrum. This is particularly effective for reducing imaging spectrometer data to "relative reflectance" in an area where no ground measurements exist and little is known about the scene (Kruse et al. , 1985; Kruse, 1988). It works best for arid areas with no vegetation. The IARR calibration is performed by calculating an average spectrum for the entire AVIRIS scene and using this as the reference spectrum. Apparent reflectance is calculated for each pixel of the image by dividing the reference spectrum into the spectrum for each pixel.
The Empirical Line calibration technique is used to force image data to match selected field reflectance spectra (Roberts et al. , 1985; Conel et al. , 1987; Kruse et al. , 1990). This method requires ground measurements and/or knowledge. Two or more ground targets are identified and reflectance is measured in the field. Usually the targets consist of at least one light and one dark area. The same two targets are identified in the AVIRIS images and average spectra are extracted for Regions of Interest. A linear regression is calculated between the field reflectance spectra and the image radiance spectra to determine a linear transform from radiance to reflectance for each band of the AVIRIS data set. Gains and offsets calculated in the regression are applied to the radiance spectra for each pixel to produce apparent reflectance on a pixel-by-pixel basis.
The Spectral Library Input File dialog will appear to allow selection of a spectral library.
This is the spectral library containing the results from the various calibration methods.
The Spectral Library Viewer will appear with a list of the calibrated spectra (Figure 11).
Select Calibrated Spectra from Spectral Library
The spectra will be plotted in a Spectral Library Viewer plot (Figure 11). Visually compare the various calibrations and note and compare their characteristics.
What general conclusions can you draw about the quality of the different calibration procedures?
The calibrated data files for all of the different calibrations are available for spectral browsing if desired. All files have been converted to integer format by multiplying the reflectance values by 1000 because of disk space considerations. Values of 1000 in the data indicate apparent reflectances of 1.0.
| File Type | File Name |
|---|---|
| ATREM | CUP95_AT.INT |
| Flat Field | CUP95_FF.INT |
| IARR | CUP95_IA.INT |
| Emp. Line | CUP95_EL.INT |
If you are using ENVI RT, quitting ENVI will take you back to your operating system.
Conel, J. E., Green, R. O., Vane, G., Bruegge, C. J., Alley, R. E., and Curtiss, B., J., 1987, Airborne imaging spectrometer-2: radiometric spectral characteristics and comparison of ways to compensate for the atmosphere: in Proceedings, SPIE, v. 834, p. 140 - 157.
CSES (Center for the Study of Earth from Space), 1992, ATmospheric REMoval Program (ATREM), version 1.1, University of Colorado, Boulder, 24 p.
Gao, B. C., and Goetz, A. F. H., 1990, Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data: Journal of Geophysical Research, v. 95, no. D4, p. 3549-3564.
Goetz, A. F. H., Vane, G., Solomon, J. E., and Rock, B. N., 1985, Imaging spectrometry for Earth remote sensing: Science, v. 211, p. 1147 - 1153.
Goetz, A. F. H., and Srivastava, V., 1985, Mineralogical mapping in the Cuprite Mining District, Nevada: in Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop, JPL Publication 85-41, Jet Propulsion Laboratory, Pasadena, CA, p. 22-29.
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, 4th Thematic Conference on Remote Sensing for Exploration Geology, Environmental Research Institute of Michigan (ERIM), Ann Arbor, p. 309 - 324.
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, p. 31 - 51.
Kruse, F. A., Kierein-Young, K. S., and Boardman, J. W., 1990, Mineral mapping at Cuprite, Nevada with a 63 channel imaging spectrometer: Photogrammetric Engineering and Remote Sensing, v. 56, no. 1, p. 83-92.
Roberts, D. A., Yamaguchi, Y., and Lyon, R. J. P., 1985, Calibration of Airborne Imaging Spectrometer data to percent reflectance using field measurements: in Proceedings, Nineteenth International Symposium on Remote Sensing of Environment, Ann Arbor, MI, October 21-25, 1985.
Roberts, D. A., Yamaguchi, Y., and Lyon, R. J. P., 1986, Comparison of various techniques for calibration of AIS data: in Proceedings, 2nd AIS workshop, JPL Publication 86-35, Jet Propulsion Laboratory, Pasadena, CA, p. 21-30.
Vane, Gregg, and Goetz, 1985, Introduction to the proceedings of the Airborne Imaging Spectrometer (AIS) data analysis workshop: in Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop, JPL Publication 85-41, Jet Propulsion Laboratory, Pasadena, CA p. 1 - 21.
Copyright © 1993 - 1999, BSCLLC, All rights reserved. ENVI is a registered trademark of Better Solutions Consulting LLC, Lafayette, Colorado,Web: http://www.envi-sw.com, Email: envi@bscllc.com. .(Last Update, 22 March, 1999)