Region-based classification by combining MS segmentation and MRF for POLSAR images

查看全文 作　　者：Bin [1]Zhang;Guorui [2]Ma;Zhi [3]Zhang;Qianqing [2]Qin

机构地区：[1]School of Electronic Information,Wuhan University;[2]State Key Laboratory for Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University;[3]School of Public Administration,China University of Geosciences

出　　处：《Journal of Systems Engineering and Electronics》索引2013年第24卷第3期,共10页

基　　金：supported by the National Natural Science Foundation of China(61001187;41001256;40971219);the National High Technology Research and Development Program of China(863 Program)(2013 AA122301) 摘　　要：Speckle effects on classification results can be suppressed to some extent by introducing the contextual information.An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar(POLSAR) images based on the mean shift(MS) segmentation and Markov random field(MRF).First,polarimetric features are exacted by target decomposition for MS segmentation.An initial classification is executed by using the target decomposition and the agglomerative hierarchical clustering algorithm.Thereafter,a classification step based on MRF is performed by using the mean coherence matrices obtained for each segment.Under the MRF framework,the smoothness term is defined according to the distance between neighboring areas.By using POLSAR images acquired by the German Aerospace Centre and National Aeronautics and Space Administration/Jet Propulsion Laboratory,the experimental results confirm that the proposed method has higher accuracy and better regional connectivity than other classification methods. 关键词：POLSAR SAR图像区域分类 MRF MS 分割喷气推进实验室合成孔径雷达图像

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Region-based classification by combining MS segmentation and MRF for POLSAR images

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