Fast algorithm based on triplet Markov fields for unsupervised multi-class segmentation of SAR images  

作　　者：WU Yan WANG Xin XIAO Ping GAN Lu LIU ChunYan LIMing 

机构地区：[1]School of Electronics Engineering, Xidian University, Xi ' an 710071, China [2]Shaanxi Bureau of Surveying &Mapping, Xi'an 710054, China [3]National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China

出　　处：《Science China(Information Sciences)》2011年第7期1524-1533,共10页中国科学（信息科学）（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No.60872137);the National Defense Foundation of China (Grant No.9140A01060408DZ0104);the Aviation Science Foundation of China (Grant No.20080181002)

摘　　要：Non-Gaussian triplet Markov fields （TMF） model is suitable for dealing with multi-class segmentation of non-stationary and non-Gaussian synthetic aperture radar （SAN） images. Considering the complexity of the model and algorithm, as well as the requirement of real-time, and robust and efficient processing of SAR images, a fast algorithm based on TMF for unsupervised multi-class segmentation of SAR images is proposed in this paper. For the speckle noise in SAR images, numerical characteristic, threshold selection and QuadTree decomposition criterion are researched firstly. With the new method, a SAR image can quickly be mapped into an edge-based pixon-representation, which results in a coarse decomposition in smooth regions, and a fine decomposition in edges. Then by combining TMF model with the pixon-representation of SAR image, a new potential energy function of TMF based on pixon-representation is derived. Finally, the segmentation is finished by Bayesian maximum posterior mode （MPM）. The effectiveness of the fast TMF algorithm is demonstrated by applying it to simulated data and real SAR images.Non-Gaussian triplet Markov fields （TMF） model is suitable for dealing with multi-class segmentation of non-stationary and non-Gaussian synthetic aperture radar （SAN） images. Considering the complexity of the model and algorithm, as well as the requirement of real-time, and robust and efficient processing of SAR images, a fast algorithm based on TMF for unsupervised multi-class segmentation of SAR images is proposed in this paper. For the speckle noise in SAR images, numerical characteristic, threshold selection and QuadTree decomposition criterion are researched firstly. With the new method, a SAR image can quickly be mapped into an edge-based pixon-representation, which results in a coarse decomposition in smooth regions, and a fine decomposition in edges. Then by combining TMF model with the pixon-representation of SAR image, a new potential energy function of TMF based on pixon-representation is derived. Finally, the segmentation is finished by Bayesian maximum posterior mode （MPM）. The effectiveness of the fast TMF algorithm is demonstrated by applying it to simulated data and real SAR images.

关 键 词：pixon-representation of SAR image Quad Tree decomposition new potential energy function triplet Markov fields multi-class segmentation 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP391.41[自动化与计算机技术—计算机科学与技术]