  Remote sensing techniques are widely used for land cover classification and
urban analysis. The availability of high resolution remote sensing imagery
limits the level of classification accuracy attainable from pixel-based
approach. In this paper object-based classification scheme based on a
hierarchical support vector machine is introduced. By combining spatial and
spectral information, the amount of overlap between classes can be decreased;
thereby yielding higher classification accuracy and more accurate land cover
maps. We have adopted certain automatic approaches based on the advanced
techniques as Cellular automata and Genetic Algorithm for kernel and tuning
parameter selection. Performance evaluation of the proposed methodology in
comparison with the existing approaches is performed with reference to the
Bhopal city study area.
