GIS4035 Module 10: Supervised Classification

ERDAS Imagine was used to develop two different land use maps during this module, with the method of supervised classification being the focus of the exercises. In areas of interest (AOIs), polygons were drawn around representative pixels of different land uses or land covers, or a polygon was "grown" from a seed pixel to encompass a sample of a land use or land cover area. These samples ("signatures") were then used to classify the remainder of the area covered by the image. The classes were critiqued using histograms and Mean (statistically speaking) Plots, and additional signatures were gathered as needed. (That was the intent anyway. Initially I thought the classification was looking fairly decent after some adjustments, but then I noticed something fishy about the roads, including the acreage attributed to roads. Obviously there is some other feature lumped in with roads which needs to be evaluated further.) After the additional signatures were incorporated, the histograms and Mean Plots were used to critique the classification again. Bands 3, 4, and 5 had the least amount of overlap, and so various combinations of them were used to edit the signature colors before finally settling on R3, G4, B5 for the Germantown map shown below. The image was classified with this band combination, and a distance file was created. The purpose of the distance file was to highlight specific areas which might have been misclassified. Similar classes were merged by the process of recoding, and areas were calculated.

Supervised Land Use Classification
Germantown, Maryland