Supervised Classification

This map shows a supervised classification to identify land use and land cover in Germantown, MD for the purposes of tracking changes in population and land consumption.  An image of the area was used to create signatures to be used in classification, and based on those signatures, the image was classified and the signatures merged into 8 classes.  The area for each class was calculated so that, when compared with images from previous or later years, the changes can be calculated.  

Creating signatures requires paying attention to what type of process you use for each signature.  Sometimes it is better to draw a polygon, other times Growing a Seed is the preferred method.  In general, the second method seemed to be best, although for the Fallow Field 1 signature, the polygon was a good choice.  By looking at histograms of each signature, it was possible to see if there was a good enough sample of pixels, and if there was separation in the spectral signature between classes.  By using the Mean Plot it was possible to determine the best band combination to create separation between each spectral signature.

With more time, even better signatures could have been created, but the distance image shows there were not too many poorly classified areas.