Unsupervised Classification

This is an example of unsupervised classification. Here a Landsat image was processed so that clusters of similar pixels were grouped as classes, and then those classes were matched to various feature types in the original image.  The result is a thematic map representing, in this case, 5 classes.  The classes can be analyzed to answer questions; in this case the question was what percentage of the image represented impermeable versus permeable surface.  The necessary information was contained in the Attribute table for the newly created classification image.  One issue that came up was that some pixels were classified in more than one group - for example there were green grass pixels on the roofs of some buildings.  The solution was to create a "Mixed" class that would allow for those situations where classification into a clearly identified group was not possible.