Unsupervised multi-class segmentation of SAR images using fuzzy triplet Markov fields model

Authors:
Peng Zhang;Ming Li;Yan Wu;Lu Gan;Ming Liu;Fan Wang;Gaofeng Liu
Affiliations:
National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China;National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China;Remote Sensing Image Processing and Fusion Group, School of Electronic Engineering, Xidian University, P.O. Box 140, Xi'an, Shaanxi 710071, China;Remote Sensing Image Processing and Fusion Group, School of Electronic Engineering, Xidian University, P.O. Box 140, Xi'an, Shaanxi 710071, China;Remote Sensing Image Processing and Fusion Group, School of Electronic Engineering, Xidian University, P.O. Box 140, Xi'an, Shaanxi 710071, China;Remote Sensing Image Processing and Fusion Group, School of Electronic Engineering, Xidian University, P.O. Box 140, Xi'an, Shaanxi 710071, China;National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China
Venue:
Pattern Recognition
Year:
2012

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Full Length Article: SAR image multiclass segmentation using a multiscale and multidirection triplet Markov fields model in nonsubsampled contourlet transform domain

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Abstract

Triplet Markov fields (TMF) model proposed recently is suitable for nonstationary image segmentation. For synthetic aperture radar (SAR) image segmentation, TMF model can adopt diverse statistical models for SAR data related to diverse radar backscattering sources. However, TMF model does not take into account the inherent imprecision associated with SAR images. In this paper, we propose a statistical fuzzy TMF (FTMF) model, which is a fuzzy clustering type treatment of TMF model, for unsupervised multi-class segmentation of SAR images. This paper contributes to SAR image segmentation in four aspects: (1) Nonstationarity of the statistical distribution of SAR intensity/amplitude data is taken into account to improve the spatial modeling capability of fuzzy TMF model. (2) Mean field theory is generalized to deal with planar variables to derive prior probability in fuzzy TMF model, which resolves the problem in Gibbs sampler in terms of computation cost. (3) A fuzzy objective function with regularization by Kullback-Leibler information of fuzzy TMF model is constructed for SAR image segmentation. The introduction of fuzziness for the belongingness of SAR image pixel makes fuzzy TMF model be able to retain more information from SAR image. (4) Fuzzy iterative conditional estimation (ICE) method, as an extension of the general ICE method is proposed to perform the model parameters estimation. The effectiveness of the proposed algorithm is demonstrated by application to simulated data and real SAR images.