Module 10 - Supervised Classification of Germantown, MD

The first portion of this lab allowed us to experiment with a supervised classification.  We used tools such as the signature editor, imported or created and AOI layer, used UTM coordinates to hone in on classification types and used the polygon method versus the grow/grow properties option to "train" the classification tool.  After our first attempt we used the histogram plots and Mean plots to evaluate the results of our spectral signature.  We set the signature colors to approximate true colors using a band combination that would have the least spectral confusion.We then applied and saved our signature file.  We then used the Classify-Supervised tool to classify our image.  We then merged multiple classes of a similar nature to narrow down our actual classes.  Once complete we generated a distance image and a recoded image.  We then used the attribute table for the recode image to add our class names and calculate the area.  It took me a few tries to get the entire process good results.  I then moved on to the actual assignment which was to perform a supervised classification of Germantown MD.  Here is my end result: