An improved unsupervised classification scheme for polarimetric SAR image with MCSM-Wishart
CHEN Jun1, DU Peijun1,2, TAN Kun1
1. NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China;
2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
To tackle the problems of insufficiently extracting polarimetric information from PolSAR image and low classification accuracy of H/Alpha/A-Wishart unsupervised classification algorithm, this paper proposes an adapted algorithm named MCSM-Wishart by imposing multiple-component scattering model (MCSM)decomposition to fit unsupervised classification of polarimetric SAR image. Firstly, various kinds of polarimetric information such as volume scatter, double scatter, helix scatter, surface scatter and wire scatter can be extracted from the image by MCSM decomposition, and iterative self-organizing data analysis(ISODATA)technique is used for clustering. Then iterative classification based on complex Wishart distribution is used to obtain the final result. H/Alpha-Wishart, H/Alpha/A-Wishart, MCSM-Wishart and supervised-Wishart algorithms are compared with each other based on two research plots conducted respectively in Lishui of Nanjing City and Binhai Wetland of Yancheng City with PALSAR image from ALOS. The results show that MCSM-Wishart classification algorithm can improve to a certain extent the original classifiers in terms of efficiency, total accuracy and Kappa coefficient. It is therefore concluded that the polarimetric information extracted by MCSM decomposition can sufficiently reflect the characteristics of the ground object. Combining with ISODATA clustering algorithm, MCSM decomposition can be used in the iterative classification based on complex Wishart distribution so as to improve the classification accuracy and reliability efficiently.
陈军, 杜培军, 谭琨. 一种改进的全极化SAR图像MCSM-Wishart非监督分类方法[J]. 国土资源遥感, 2015, 27(2): 15-21.
CHEN Jun, DU Peijun, TAN Kun. An improved unsupervised classification scheme for polarimetric SAR image with MCSM-Wishart. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 15-21.
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2015, Vol. 27 
