Lab 14: Unsupervised Classification
Classification is the process of grouping pixels, based on their DN’s, into spectral and/or
informational classes that capture a theme (e.g., land cover, surface geology) that we
are interested in mapping. Unsupervised classification uses statistical clustering to
group pixels. Today you will use unsupervised classification to classify the land cover
arounc Ocean Lake, near Riverton, Wyoming.
Note that you will save your final classifications today for comparison to the
supervised classification you will create in the next lab exercise!
1. Open the Ocean Lake image (lab14_unsupervised folder), display it as a false
color IR image (Layers are 1=blue, 2=green, 3=red, 4=NIR, 5 and 6 = MIR), and
familiarize yourself with the land cover types found there. This is an agricultural
area, with a mixture of irrigated and fallow fields surrounding Ocean Lake, as
well as a few small settlements. Natural vegetation is mostly sagebrush and
grass, and there is some bare ground. Several rivers and streams are in the area
too. Remember that you can use Google Earth to get a view of the area, and
there may be photos linkded to Google Earth to help you better understand
what the land cover looks like. (Worksheet Question #1).
2. Now, we will use the unsupervised classification to create a map with 5 classes.
From the main Erdas menu bar choose the Raster tab and in the Classification
area choose Unsupervised/Unsupervised Classification. The Ocean Lake image
is your input file; browse to your directory and type in a meaningful name (e.g.,
ocean_lake_unsup_5_class.img) for the output file. Uncheck the “Output
Signature Set” box if it is checked. Be sure the “Initialize from Statistics” button
is set. Set the number of classes to 5 and the number of iterations to 10. In the
Color Scheme Options choose “Approximate True Color.” This colors your
output classes to the average color of the image you have displayed. Run the
classification. The routine will end when either the 95% of the pixels remain in
the same class on successive iterations of the algorithm or when you achieve 10
iterations, whichever happens first.
3. Open another Viewer window and view the result as a pseudocolor image.
Carefully examine the clusters (classes) and ascertain if there is confusion among
the types (e.g., does the irrigated agriculture class always correspond to irrigated
agriculture, or are other land cover types classified as irrigated ag, etc.). If
necessary, you can right-click on the filename for your classified image in the
Contents area and choose “Display Attribute Table.” When it appears at the
bottom, you can change the color used to display a class to something easily
seen (e.g., yellow) to better assess each class. (Worksheet questions #2 & 3).
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4. Remote sensing scientists often create more classes than they plan to end up
with in their final map. (Worksheet Question #4)
Repeat the above process (#2), but this time classify the image into 10 classes.
When you are done, display the image as you did before and then change the
class colors so that one color represents one type on the ground (by coloring
similar informational classes the same color). Compare the result to the 5-class
classification and to the unclassified image. (Worksheet questions #5 & 6).
NOTE: Save your unsupervised classifications in your H:\ directory so that you can
compare them to the supervised classification you will produce in the next lab.
OPTIONAL: If you have time you can experiment with variations to see if you can
improve your classification. What if you calculate NDVI and classify that? What if
you use only SOME of the satellite bands rather than all of them?
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Name_____________________________________
Worksheet
Lab 14: Unsupervised Classification
1. Make a list of the MAIN cover types that you can distinguish in this image and try
to aggregate these into 5 general classes. List these classes here, and speculate
about whether you think they are spectrally distinct from the other 4 classes. If
not, what might they be confused with, spectrally?
2. How does your 5-class unsupervised classification compare to your
knowledge of what is really on the ground?
3. Are there any types that are confused with one another in your 5-class
classification? If so, list them and suggest a reason for the confusion?
4. Why might it be advantageous to create more classes than you need? Explain
this in terms of spectral confusion.
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5. Did your 10-class classification solve any of the confusion seen in your 5-class
classification? If so, what problems were solved. Are there any areas that stand
out that you couldn’t see before?
6. If it did, why do you think the 10 class classification did a better job? If it didn’t,
what is worse and why? In this area, do spectral classes correspond well to
informational classes?
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