‫بسم هللا الرحمن الرحيم‬
King Saud University
College of Engineering
Civil Engineering Department
SE 461: “Remote Sensing Principles”
1428/1429H 2nd Semester
Lab Assignment # 3
Unsupervised Classification
The ISODATA Classifier
In this lab assignment we will conclude our investigation of land cover classifications by exploring the
unsupervised classification. This method is an unsupervised classifier because the algorithm
automatically determines the class signatures to use in creating the classification; however, the user still
governs how many classes into which the algorithm will organize the data. Until now, we have tried three
classifiers (the Box, the MDM, & the ML). Now, we will use an unsupervised classifier called Iterative
Self Organizing Data Analysis (ISODATA). This algorithm will provide us with a classified image as
well as the signatures for each of the classes. We will be using ERDAS Imagine.
Copy the file “mad_spot.img” to your working directory.
Part I. Iterative Self Organizing DATa Analysis (ISODATA)
A. Select Classifier from the launch pad & choose Unsupervised Classification.
B. Set the Input Raster File to your copy of the “mad_spot.img " file.
C. Set the Output Cluster Layer to a file called "isodata.img" in your working directory. This file will
contain the classified image output from the ISODATA algorithm.
D. Set the Output Signature to a file called "isodata.sig" in your working directory. This file will contain
the spectral signatures created by the ISODATA algorithm.
E. Click on Initializing Options & note the default parameters used for initializing the class means.
F. Change the Number of Classes to 20 & the Maximum Iterations to 10.
G. Note the default values listed in the Processing Options section. The Maximum Iterations &
Convergence Threshold parameters will be used to control when the algorithm should stop. The
Maximum Iterations is the greatest number of times the algorithm will loop through the ISODATA
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processing logic. The Convergence Threshold is a percentage of the classified image that must remain
unchanged between two successive iterations for the algorithm to halt. Briefly describe how the
ISODATA algorithm will classify the input imagery. Be sure to include comments about where the
locations of the initial class means will be set & why there are two rules to stop the processing.
H. Click OK. Watch the status bar carefully & note how many iterations it takes to complete the
classification. Which of the two criteria (number of iterations or percent change) was used to stop
the algorithm?
Part II. From Classes to Information
Now you've got 20 classes from the ISODATA algorithm. One of the most difficult tasks of using an
unsupervised classifier is assigning information classes to the numeric classes of the classified image.
Let's see if we can make some sense of these classes.
A. Display the classified image contained in the file "isodata.img".
B. You can determine where the different classes are within the image by finding the corresponding for
each the six classes that you defined in Lab Assignment 1 (i.e. Water, Grass, Wetland, Forest,
Impervious Surface, Crop). How many unique information classes do there appear to be? Give an
example of any two numeric classes that seem redundant (i.e. belong to the same information
class). Which information classes seem unseparable? In situations where two or more information
classes are unseparable, which class should take precedence & why? Debate whether or not
increasing the number of classes created by the ISODATA algorithm would help in discriminating
between “unseparable” classes (Hint: Classifier  Signature Editor, File  Open, “isodata.sig”,
View  Mean Plots).
C. From the Viewer #1 window, select File  Close.
Part III. Class Fragment Reduction
You may note that the classes are highly fragmented in some areas. This spatial fragmentation might be
undesirable if you were going to vectorize this image (raster to vector conversion) or if you had some
minimum mapping unit criterion which must be met.
One way to reduce this fragmentation is to pass a convolution filter (kernel) over the image.
A. Make a copy of your “isodata.img” file in your working directory & name it "isodataflt.img".
B. Select Viewer, & display the classification image in the “isodataflt.img” file.
C. Select Raster  Filtering  Statistical Filtering.
D. Set the Function to Majority & the Window Size (kernel size) to 3 x 3.
E. Click Apply & note what happens to the image. You can undo this change by selecting Raster 
Undo. What happened to the class fragments? What happened to the class boundaries? What
happens if a larger window size is used? Why didn't we use a mean or median value for the output
function of the filter?
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