Classification of Polarimetric SAR Images Based on Support Vector Machine

Compared with conventional maximum likelihood(ML) classifier,classification performance of support vector machine(SVM) is still good with small training samples.SVM is widely used in several fields recently.In this paper,SVM is used in classification of polarimetric SAR images based on feature extraction,and effect of several important parameters of SVM on classification performance is analyzed.The performance of SVM is compared with ML using L-band fully polarimetric SAR data of san francisco,acquired by the NASA/JPL AIRSAR sensor.A classification map of dual-polarization data,obtained by China,based on SVM is also presented.