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

作　　者：Bin Zhang Guorui Ma Zhi Zhang Qianqing 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》2013年第3期400-409,共10页系统工程与电子技术（英文版）

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 sup- pressed 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, polarimetdc features are exacted by target decomposition for MS segmentation. An initial classification is executed by using the target decomposition and the agglomerative hierarchical clus- tering 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.Speckle effects on classification results can be sup- pressed 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, polarimetdc features are exacted by target decomposition for MS segmentation. An initial classification is executed by using the target decomposition and the agglomerative hierarchical clus- tering 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：polarimetric synthetic aperture radar （POLSAR） clas-sification maximum a posteriori （MAP） mean shift （MS） Markov random field （MRF）. 

分 类 号：TN958[电子电信—信号与信息处理]