THE INTEGRATION OF GEOGRAPHIC DATA WITH REMOTELY SENSED IMAGERY TO IMPROVE CLASSIFICATION IN AN URBAN AREA

This paper investigates the incorporation of ancillary spatial data to improve the accuracy and specificity of a land-use classification from Landsat Thematic Mapper (TM) imagery for nonpoint source pollution modeling in a small urban area -- the city of Beaver Dam, Wisconsin. A post-classification model was developed to identify and correct areas of confusion in the Landsat TM classification. Zoning and housing density data were used to modify the initial classification. Land-use classification accuracy improved and the number of identifiable classes increased. Additionally, confusion between classes that were commonly misclassified (for example, commercial and industrial areas) was reduced.