HIGH RESOLUTION POLSAR IMAGE CLASSIFICATION BASED ON GENETIC ALGORITHM AND SUPPORT VECTOR MACHINE
- The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing of Wuhan University, 430079, No.129 Luoyu Road, Wuhan, P. R. China
Keywords: PolSAR, Classification, Feature Selection, GA, SVM
Abstract. This paper focuses on backscattering mechanisms selection and supervised classification works for CETC38-X PolSAR image. Thanks to the high radar resolution, many classes of man-made objects are visible in the images. So, land-use classification becomes a more meanful application using PolSAR image, but it involves the selection of classifiers and backscattering mechanisms. In this paper we apply SVM as the classifier and GA as the features selection method. Finally, after we find the best parameters and the suitable polarimetric information, the overall accuracy is up to 97.49%. The result shows SVM is an effective algorithm compared to Wishart and BP classifiers.