EA 2003
Workshop 5, Classifying satellite imagery 1
J. M. Stroh
Objectives: In this exercise you will learn to use two unsupervised classification tools,
and see their limitations. Unsupervised means the software does the classification without
the operator's intervention.
Background.
You will use a portion of the MSS image from a previous exercise (workshop #3). The
image shows the white snow still present in the high Sierra Nevada mountain range of
eastern California. To the east of the mounts lies Owens Valley, with the Owens River
flowing through part of it. Large alluvial fans spread out onto the valley from canyons.
To the east of Owens Valley lies another mountain range, the White-Inyo Mountains. The
scene has great complexity. Owens Valley has an arid climate. Most of the vegetation on
the alluvial fans and valley floor has a large proportion of soil exposed. But, dense
vegetation grows where water is available. This produces a wide variety of vegetation
associations. The some of these associations are irrigated fields of alfalfa, irrigate
meadows, spring and seep meadows, alkali shrubland (fed by groundwater), and riparian
zones along streams and on some parts of the alluvial fans.
Your job is to find the areas in Owens Valley that have obvious healthy, dense, green (in
real life) vegetation.
Place your responses to questions in the week 5 GIS folder in the drop box.
Copy the data folder MSSw data from the Week 5 folder on the program share on Masu
to C:\temp.
The composite image comp8-w shows healthy, dense, vegetation as red. Study it for a
while. The irrigated fields of alfalfa are particularly easy to see, as are some seep areas.
Most of the scene does not have dense vegetation.
Read about the module Cluster in Help. Cluster groups pixels by radiation number and
frequency. It works well to find the dominant components of an image. Run Cluster on
comp8-w in course mode, then in fine mode with the default settings. Call the output
images anything you want.
Did Cluster in coarse mode find the vegetation as a distinct category? Why or why not?
Did Cluster in fine mode find the vegetation as a distinct category? Why or why not?
There is an important lesson here.
Another powerful classification tool is Principal Component Analysis (PCA). It is a highpowered multivariate statistical method designed to fined the fewest important factors in
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a data set (in this case an image). Read about PCA in Help. Also note its use for destriping satellite imagery. Run PCA with the 4 images MSS1w through MSS4w, set the
number of components to 3, and leave the other settings at defaults. Use any prefix you
want for the names of the images (there will be 3 of them). After they appear save the
module results as a file with the name PCAresults. Try to understand some of the
information in the module results.
View the 3 output PCA images Did PCA find the vegetation as a distinct category? Why
or why not? But, did PCA simplify the image as it should? How can you tell (look at the
PCA tabular output).
PCA is a very powerful simplification tool based on statistics. It works very well for
unsupervised classification, but classification of what?
A second try with Cluster.
Open and study the composite image Bigpine. It is a small piece of the Comp8-w image.
See the last page of this workshop to see some of the major land cover types near
Bigpine. Notice all the nice red vegetation. Run Cluster in course and fine modes on the
image Bigpine just as you did earlier on the larger images (set fine to default - drop least
significant clusters and view with the Qualitative 256 palette). Call the output of the
course cluster image anything you want , and call the fine cluster image vegcluster.
Just as before:
Did Cluster in coarse mode find the vegetation as a distinct category? Why or why not?
Did Cluster in fine mode find the vegetation as a distinct category? Why or why not?
There is an important lesson here too, but a different one. What is this simple but
important lesson?
The fine cluster should have separated most of the well-watered vegetation out quite
nicely. We can ask, how much of the area of the scene is healthy vegetation (according to
the remote image analysis)?
To find the area of well-watered vegetation on vegcluster:
1. Determine which categories in the legend refer to the well-watered vegetation.
2. Use the Reclass module to change these classes to a value of 1 and all other classes to
a value of 0 (a Boolean image). Call the Boolean image anything you want.
3. View the Boolean image to be sure the classification looks reasonable (area of
vegetation properly selected in the reclassification).
4. Run the Area module on the Boolean image and determine the area in square
kilometers.
What is the area of well-watered vegetation?
What percent of the total area is this?
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Area in Owens Valley near Big Pine California.
Valley bottom
Spring and irrigation
Fields
River
Cinder
cone
Lake
Alluvium
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