Urban Land Cover Mapping from Kompsat EOC Panchromatic Images Using an Object-Oriented Classification Approach

High resolution panchromatic satellite images collected by sensors such as IRS-1C/D and KOMPSAT-1 have a spatial resolution of approximately 6 x 6 m², making them very attractive for urban applications. However, the spectral information present in these images is very limited. In order to overcome this limitation an object-oriented classification approach is adopted to identify basic land cover types in urban areas. Before an image is classified it is segmented into image objects at different aggregation levels using a multiresolution segmentation approach. In the course of the segmentation various statistical information as well as topological information for each object is collected. Based on this information it is possible to classify the image objects and to arrive at much better results than by looking only at single pixels. Using an image recorded by KOMPSAT-1 over the City of Vienna a land cover classification was carried out for two areas. One was used to set up the rules for the different land cover types. The second subset was classified based on these rules, only adjusting some of the functions governing the classification.