Remote sensing information extraction based on object-oriented and support vector machines

It is the important for remote sensing image processing to quickly,efficiently and accurately extract the land use/cover information.Traditional remote sensing classification makes use of the single-source spectral information to extract the land use/cover information without spatial information.In this paper,Object-oriented and Support Vector Machines are incorporated to extract the land use/cover information with spectral and texture information.Object-SVM model is set up for the extraction of land use/cover information in area coverage,comparing with Object-oriented fuzzy logic and SVM based on pixels.The research shows that Object-SVM can achieve better accuracies and efficiencies for actual applications.