Supervised Classification

Supervised Classification using Bands 4, 5, and 6

The final regular season lab for Remote Sensing was a supervised classification of a developing city (Germantown in Maryland).  We began with an aerial image of the town along with coordinates for 12 known land cover types.  After locating these points in the image, I used the technique of growing spectral signatures from a seed.  This involves setting a Spectral Euclidean Distance (mostly an estimate at first) and letting ERDAS Imagine build a polygon of pixels starting at the point you designate and emanating out to the spectral distance you selected.  This produces very interesting results and it is both fun and useful to play a bit with the distance to get the best signature of the known terrain.

Once the signatures are complete, it is important to verify that there is no spectral confusion (overlap) between signatures.  I found the widest separation in the signatures was found in Bands 4, 5, and 6, so I used these bands to create the supervised image.  I also merged the like signatures (we had multiple signatures for the same land cover, like "agriculture") into single classifications. Creating a set of classes also allows one to estimate the area dedicated i the image to each class.  The final supervised classification is presented above.