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《Science of Surveying and Mapping》 2013-03
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A SVM-based classification method for fully polarimetric SAR imagery considering feature optimization

WU Zhao-cong ①,OUYANG Qun-dong ①,LI Fang-fang ②(①School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,China;②Key Lab of C4ISR Technology of National Defense Science and Technology,National Univ.of Defense Technology,Changsha 410073,China)  
An improved SVM classification method for fully polarimetric SAR imagery concerned with feature optimization was proposed in this paper,which is based on the assessment of support vector number and correlation analysis,and combining with SFS search strategy.Experimental results showed that the proposed method could not only retain good generalization with limited samples,but also obtain higher classification accuracy with less number of features in a wider range of SVM parameters.
【Fund】: 国家863计划资助项目(2007AA12Z143);; 国家自然科学基金资助项目(40201039 40771157 41001260)
【CateGory Index】: P225
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