Influence of Geometric and Graph Theoretical Measures on Land Classification Using High-Resolution Remote Sensing Images

We focus on geometric structure and neighborhood information in high spatial resolution images,combining with local situation and spatial distribution of land use,to accomplish land use classification using geometric and graph theoretical measures based on a SVM,and discuss how these measurements affect the classification results.The experiments use QuickBird images.The result shows that this method can classify the sampled images into four typical classes as rural,agricultural,industrial and commercial-residential regions.The final results can also be used for land use change detection and monitoring.