Polarimetric SAR Image Classification using Multiple-Component Scattering Model and Support Vector Machine

摘要：

The classification of polarimetric SAR image based on Multiple-Component Scattering Model (MCSM) and support Vector Machine (SVM) is presented in this paper.MCSM is a potential decomposition method for a general condition.SVM is a popular tool for machine learning tasks involving classification,recognition or detection. The scattering powers of singlebounce,uouble-bounce,volume,helix,and wire scattering components ar.extracted from full polarimetric SAR images.Combined with the scattering power and the texture feature,SVM is used for the polarimetric classification.We generate a validity test for the method using EMISAR L-band full polarized data of Foulum Area (DK),Denmark. The preliminary result indicates that this method can classify most of the areas correctly.

关键词： Polarimetric SAR (PolSAR) Classification Multiple-Component Scattering Model (MCSM) Support Vector Machine (SVM)

作者: Lamei Zhang Bin Zou Qingchao Jia Ye Zhang

作者单位: Department of Information Engineering,Harbin institute of Techonology,Harbin,heilongjiang,China

会议类型: 国际会议

会议名称: 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar(第二届亚太合成孔径雷达会议)

会议地点: 西安

会议语种:英文

页码: 805-808

在线出版日期: 2009-10-26（万方平台首次上网日期，不代表论文的发表时间）