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《Science Technology and Engineering》 2014-17
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A Supervised Classification Algorithm Based on Pauli Decomposition and SVM on Polarimetric SAR Image

CHEN Jun;DU Pei-jun;TAN Kun;Jiangsu Key Laboratory of Resources and Environmental Information Engineering(China University of Mining and Technology);Jiangsu Key Laboratory of Resources and Environmental Information Engineering,(China University of Mining and Technology);Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology,Nanjing University;Jiangsu Key Laboratory of Resources and Environmental Information Engineering1,(China University of Mining and Technology);  
An important precondition of accurate classification on Polarimetric SAR image is sufficient feature extraction which can reflect ground objects' physical attributes. However,there's many feature extraction and classification algorithms for polarimetric SAR image,which have all kinds of problems. Both polarimetric feature extraction methods and classification algorithms can affect the final classification accuracy. Aiming at this problem,on the basis of many experiments,a new classification strategy called Pauli-SVM for short is proposed by synthesizing Pauli polarimetric feature decomposition and SVM algorithm. Firstly polarimetric features extracted from classic Pauli decomposition including odd scattering,double scattering and volume scattering are used to form an eigenvector. Secondly,after training samples are selected,supervised classification can be done on polarimetric SAR image by importing SVM algorithm which can get high classification accuracy. Finally,experiments of contrasting supervised Wishart algorithm,SVM algorithm combined by Freeman feature extraction method,SVM algorithm combined by Yamaguchi feature extraction method and Pauli-SVM algorithm are done on two research plots including Lishui in Jiangsu province and Hengxi Town in Nanjing city with PALSAR image from ALOS satellite. The result turns out that new proposed Pauli-SVM algorithm can efficiently promote classification accuracy.
【Fund】: 国家自然科学基金(41171323);; 中国地质调查局地质调查工作项目(1212011120229);; 江苏省自然科学基金(BK2012018);; 地理空间信息工程国家测绘地理信息局重点实验室开放基金(201109)资助
【CateGory Index】: TN957.52
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