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

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.

[1]  W. Freeman,et al.  Generalized Belief Propagation , 2000, NIPS.

[2]  Dirk H. Hoekman,et al.  Initialization of Markov random field clustering of large remote sensing images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Sotirios Chatzis,et al.  A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation , 2008, IEEE Transactions on Fuzzy Systems.

[4]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Song-Chun Zhu,et al.  Statistical Modeling and Conceptualization of Visual Patterns , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Frédéric Galland,et al.  Synthetic aperture Radar oil spill segmentation by stochastic complexity minimization , 2004, IEEE Geoscience and Remote Sensing Letters.

[7]  Fang Liu,et al.  Spectral Clustering Ensemble Applied to SAR Image Segmentation , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[8]  David A. Clausi,et al.  SAR Sea-Ice Image Analysis Based on Iterative Region Growing Using Semantics , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Christophe Collet,et al.  Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Henri Maître,et al.  A new statistical model for Markovian classification of urban areas in high-resolution SAR images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Rob J. Dekker,et al.  Texture analysis and classification of ERS SAR images for map updating of urban areas in The Netherlands , 2003, IEEE Trans. Geosci. Remote. Sens..

[12]  Abdelwaheb Marzouki,et al.  Estimation of generalized mixtures and its application in image segmentation , 1997, IEEE Trans. Image Process..

[13]  Ning Situ,et al.  A narrow band graph partitioning method for skin lesion segmentation , 2009, Pattern Recognit..

[14]  Martin J. Wainwright,et al.  MAP estimation via agreement on trees: message-passing and linear programming , 2005, IEEE Transactions on Information Theory.

[15]  Martial Hebert,et al.  Discriminative Random Fields , 2006, International Journal of Computer Vision.

[16]  Shaun Quegan,et al.  Quantitative comparison of the performance of SAR segmentation algorithms , 1998, IEEE Trans. Image Process..

[17]  Kanti V. Mardia,et al.  Spatial Classification Using Fuzzy Membership Models , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Wojciech Pieczynski,et al.  Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Gersende Fort,et al.  Combining Monte Carlo and Mean-Field-Like Methods for Inference in Hidden Markov Random Fields , 2007, IEEE Transactions on Image Processing.

[20]  Jean-Marie Nicolas,et al.  Unsupervised segmentation of SAR images using Triplet Markov fields and fisher noise distributions , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[21]  Sadaaki Miyamoto,et al.  Fuzzy c-means as a regularization and maximum entropy approach , 1997 .

[22]  Gilles Celeux,et al.  EM procedures using mean field-like approximations for Markov model-based image segmentation , 2003, Pattern Recognit..

[23]  Ujjwal Maulik,et al.  Unsupervised Pixel Classification in Satellite Imagery Using Multiobjective Fuzzy Clustering Combined With SVM Classifier , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[24]  C. H. Chen,et al.  Handbook of Pattern Recognition and Computer Vision , 1993 .

[25]  Peng Zhang,et al.  Unsupervised multi-class segmentation of SAR images using triplet Markov fields models based on edge penalty , 2011, Pattern Recognit. Lett..

[26]  Xin Wang,et al.  Fast algorithm based on triplet Markov fields for unsupervised multi-class segmentation of SAR images , 2011, Science China Information Sciences.

[27]  Christophe Collet,et al.  Non-stationary fuzzy Markov chain , 2007, Pattern Recognit. Lett..

[28]  Haluk Derin,et al.  Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  Florence Forbes,et al.  Hidden Markov Random Field Model Selection Criteria Based on Mean Field-Like Approximations , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Ping Zhong,et al.  A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[32]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[33]  Gabriele Moser,et al.  Dictionary-based stochastic expectation-maximization for SAR amplitude probability density function estimation , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Pierre Soille,et al.  Morphological Texture Features for Unsupervised and Supervised Segmentations of Natural Landscapes , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Josiane Zerubia,et al.  Supervised segmentation of remote sensing images based on a tree-structured MRF model , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Jun Zhang,et al.  The mean field theory in EM procedures for blind Markov random field image restoration , 1993, IEEE Trans. Image Process..

[37]  David A. Clausi,et al.  Preserving boundaries for image texture segmentation using grey level co-occurring probabilities , 2006, Pattern Recognit..

[38]  Hidetomo Ichihashi,et al.  Fuzzy c-means clustering with regularization by K-L information , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[39]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  B. Frey,et al.  Transformation-Invariant Clustering Using the EM Algorithm , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Pierre Lanchantin,et al.  Statistical image segmentation using triplet Markov fields , 2003, SPIE Remote Sensing.

[42]  Wojciech Pieczynski,et al.  Unsupervised image segmentation using triplet Markov fields , 2005, Comput. Vis. Image Underst..

[43]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Anthony J. Yezzi,et al.  Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification , 2001, IEEE Trans. Image Process..

[45]  Jun Zhang.  The mean field theory in EM procedures for Markov random fields , 1992, IEEE Trans. Signal Process..

[46]  Jean-Marie Nicolas,et al.  Unsupervised Synthetic Aperture Radar Image Segmentation Using Fisher Distributions , 2009, IEEE Transactions on Geoscience and Remote Sensing.