Lab 3: Unsupervised Classification

Goal and Background:

The goal of this lab is to perform unsupervised classification on a satellite image of the Chippewa and Eau Claire counties.  Land cover classification is an important aspect of remote sensing because it provides valuable information and can be done with relative ease with today's technology.  There are many different methods and they all have their ups and downs.  Their accuracy all varies.  Unsupervised classification is the first type of classification in this class because many consider it the most simple for the user.

Methods:

In this lab, the unsupervised ISODATA classification method was used.  To begin the user brings their image into ERDAS and opens the unsupervised classification tool.  This is where they set what kind of classification they want to do and other settings such as clustering options, iterations, and color schemes.  In this case, iterations was set to 250, convergence threshold was set to .95, and the approximate true color was set for the color scheme and classes was set to 10.  This means the classification will produce ten different land cover classes.  The tool is then ran and it produces a land cover classification with ten different classes.  The next step is for the user to go through the ten different classes and choose what category they fit into. 

This is done by highlighting the class is a color like gold, and assigning it a class name from the table above.  Once they are all categorized classes that should be merged are merged together.  Google earth pro was used in assistance to see what classes were what. 

This process was done again except 20 classes were used this time and a smaller convergence threshold was used.
 Results:

The resulting image from ISODATA unsupervised classification was not great.  Just looking at it it gives a general idea of what the area looks like but it really is not accurate.  There are many agriculture areas that should be forest and vice versa.  This is also the case for urban, bare soil, and ag areas.  On the middle eastern part of the image, there are many areas that were classified as urban that are really agriculture areas that did not have crops on them at the time the image was taken.  This brown looking area was then classified as urban because its signature is similar to that of bare soil. 

Unsupervised classification does not seem like the best way to classify an image.  It might be possible with many many classes that an accurate classification can be done.  20 classes are not enough to have an accurate classification. 


Sources:

Cyril Wilson