BOT/GEOG 4211/5211 Advanced Remote Sensing of the Environment
Lab 8: Identifying Materials Using Hyperspectral Data
In this lab we will continue using the Erdas Spectral Workstation and the project that you saved
from the last lab. We will use spectral matching tools to identify materials and map where they
occur in the Cuprite, Nevada AVIRIS scene.
In brief, you will:
1. Identify materials in unknown pixels within the Cuprite, Nevada AVIRIS image
2. Map the pixels in the AVIRIS image that contain a particular material.
Be sure to turn in your answers to the lab questions (Worksheet at end) when you are
finished.
Identification of the composition of unknown pixels (Material Identification in Erdas)
Determining the composition of unknown pixels in an image is accomplished using spectral
matching techniques. The spectral reflectance of an unknown pixel is compared to reflectance
spectra from a spectral library to determine the best match or matches in ranked order.
Similarity is determined using one of several mathematical algorithms (e.g., the spectral angle
mapper, etc.). In this portion of the exercise you will try to determine which of several varieties
of alunite, a sulfate mineral used to make alum (used for everything from water purification to
underarm deodorant!), occurs in a pixel from the Cuprite image.
1. Start Erdas, open the spectral analysis workstation, and open your saved project file
(*.iwp) from the last lab. Remember that this is a bigger-than-usual dataset, and it may
take a while to open. Note that you can arrange the size of the three image windows
and the spectral curve window to your liking if you can’t see well enough in the default
window sizes. Go into the “bad bands” tool and load your saved bad bands file if it
doesn’t load automatically. Be sure that the “Use” box is checked.
2. In the list of spectral libraries on the left side of the screen, expand the USGS library by
clicking the “+” sign. Note that you can expand this window to the right so that you can
read the names of the various mineral spectra that are included.
a. Scroll down and click on the first Alunite entry, then shift-click on the last
Alunite entry to highlight all the Alunite variations in the list.
b. Left click and hold on this list, and drag selected alunites into Material List 1 in
the window below (A plus sign should appear next to it indicating multiple
spectra in the list).
c. Right click the book symbol by Material List 1, choose rename (you may have to
wait a minute for Erdas to cogitate) and then rename the list “Alunites.”
d. Repeat for the JPL spectral library, dragging all three of those Alunite spectra
into your Alunite library. You should now have 9 alunite spectra in the library.
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BOT/GEOG 4211/5211 Advanced Remote Sensing of the Environment
3. Now you will identify the unknown pixel that you are going to investigate.
a. Right click in the zoom box and choose “Inquire cursor.” In the coordinates
area, type in 337, -337 for x and y, respectively. This will place the crosshairs
directly over a single pixel. Zoom in (right click, zoom x 2, repeat) until you can
clearly see the individual pixel in the crosshairs.
b. At the top of your main screen, next to the little lock symbol, choose Red from
the color chooser (looks like a tiny coffee cup). Then select the create point
icon
and click on the exact pixel (that you found with the inquire cursor) in
the crosshairs. A spectral curve will appear at the bottom of your screen. Note
that you can right-click in this chart, choose “Chart options,” and customize the
reference grid so that you can read it more precisely.
Ignoring the bad bands, approximately where (what wavelengths) are the main
spectral features in this curve? (Worksheet question #1)
c. Close the inquire cursor dialog menu.
4. Now you will use the Material Identification module.
a.
b.
c.
d.
Click on the material identification button at the top of the screen:
.
Move the crosshairs that appear to the same pixel that you marked previously.
Click on your Alunites library at the left to select it.
Now click the lightning bolt symbol to run the routine. A dialog box will open.
Choose Spectral Correlation Map for the method, and rank the best 9 materials.
This will rank the similarity of the unknown pixel to the 9 variations of alunite in
your library.
e. Click OK to run the routine. A results window will appear at the bottom, ranking
the similarity of the unknown pixel to each of the known spectra in your library.
This may take a little while!
Which alunite best matches the spectrum for the unknown pixel? Which is the worst
match? (Worksheet questions #2)
f. From your working alunite library at the left, drag the best and worst matches
into the spectral curve window. Paste that window into this lab below (use the
snip tool). (Worksheet question #3)
Does the worst match have spectral features in different places than those of the
spectrum of the unknown pixel? How about the best match? (Worksheet question #4)
Note that in this case we “knew” (thanks to the work of other researchers) beforehand
that this pixel contained some variety of alunite. If we didn’t know that, speculate on
how you might proceed to identify the material in the pixel.
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BOT/GEOG 4211/5211 Advanced Remote Sensing of the Environment
g. When you are finished, delete all of the spectral curves from the graph at the
bottom by right-clicking on them in the graph legend on the right and choosing
delete.
Mapping the distribution of a material in the image
There are two routines available in the Erdas Spectral Analysis Workstation for mapping
the distribution of materials in an image. One, called Target Detection, is designed to
find materials that may not dominate individual pixels. The other, that we will use here,
is called Material Mapping, and is used to locate pixels that are dominated by the
material of interest. We will map the distribution of one of the alunite mineral species
in your library, called Alunite GDS82 Na82.
1. In your Alunite library on the left, select Alunite GDS82 Na82.
2. Start the Material Mapping routine by clicking the icon
at the top of the
workstation. Click the lightning bolt icon to run the application. (If the routine
doesn’t run, you may need to close Erdas and restart and then try again—there
seem to be some glitches here!)
a. In the dialog box that appears, choose Constrained Energy Minimization
for the Method, and name your output file (e.g., alunite_map). Click OK
to run the mapping routine.
b. A greyscale image will appear in the spectral workstation. Lighter colors
indicate a higher probability that the alunite occurs in that location.
Darker colors represent low probability.
Do the alunite deposits appear to be grouped, or are they dispersed
throughout the image? (Worksheet question #5)
3. The only way to know if the mapping was successful is to compare the results to
ground truth. Extensive mapping work has been completed at Cuprite, which is
an active research area, so we have ground data to compare with our map.
a. Go back to your main Erdas interface (not the spectral workstation) and
open the image (in the class data folder) called cuprite_usgs_map.img.
Note that there are several varieties of alunite mapped in the area, and
that many of these are located in the lower half of the image in reddish
colors. (I’ve reproduced the image below), though others are in olive
green.
b. You will now select just the alunite so you can compare to your map. In
the Contents window on the left, right-click the image name and choose
Display Attribute Table. Right click anywhere in the column labeled
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BOT/GEOG 4211/5211 Advanced Remote Sensing of the Environment
“Row” and choose “Select All.” Now click in one of the colored squares in
the next column and choose black. This will black out the entire image.
Next, scroll down to Row 59 and click on the “59” in the row column to
select just that row. Color it red. Similarly, scroll down to row 80 and
color it red. You should see a black screen with red places corresponding
to alunite locations. Close the attribute table (don’t save changes).
c. Now open a second viewer window and open the alunite map that you
created as a pseudocolor image. Open its attribute table and select
rows 86-255 (you can click on 86, hold down the mouse button, and drag
it down to until all rows are highlighted). Turn these rows red.
How does the pattern of alunite in your map compare to the alunite in the USGS map? If there
are mismatches, explain why the result may not perfectly match the research map. Note that
the two images are in different coordinate systems so you can’t link them or overlay them.
You’ll just have to eyeball it here. (Worksheet questions #6)
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BOT/GEOG 4211/5211 Advanced Remote Sensing of the Environment
A more thorough exploration of this image would require you to explore some of the other
algorithms to see if one works better than the others. You are encouraged to do this, and to
explore mapping and identification of other minerals in this area. The choices that you were
led to make here are somewhat arbitrary and the purpose was to introduce you to the
workflow for identifying minerals using these tools in a reasonable amount of time. You could
certainly choose to explore more careful analyses, perhaps as a semester project if you are
interested in these techniques.
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BOT/GEOG 4211/5211 Advanced Remote Sensing of the Environment
Name______________________________
Worksheet
Lab 8: Identifying Materials Using Hyperspectral Data
1. Ignoring the bad bands, approximately where (what wavelengths) are the main spectral
features in this curve?
2. Which alunite best matches the spectrum for the unknown pixel? Which is the worst
match?
3. Paste your spectral profile window below, showing the best and worse matches along with
the spectral curve for your target pixel.
4. Does the worst match have spectral features in different places than those of the spectrum
of the unknown pixel? How about the best match? Briefly discuss similarities and
differences.
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BOT/GEOG 4211/5211 Advanced Remote Sensing of the Environment
5. Do the alunite deposits appear to be grouped, or are they dispersed throughout the image?
6. How does the pattern of alunite in your map compare to the alunite in the USGS map? If
there are mismatches, explain why the result may not perfectly match the research map.
Note that the two images are in different coordinate systems so you can’t link them or
overlay them. You’ll just have to eyeball it here.
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