![]() ![]() ![]() The effectiveness of the proposed algorithm is demonstrated by application to simulated data and real SAR images. The simulation performed in EMTPWorks and MATLAB environments validates the fast response of. (4) Fuzzy iterative conditional estimation (ICE) method, as an extension of the general ICE method is proposed to perform the model parameters estimation. A hidden Markov model (HMM), a powerful pattern recognizer. The introduction of fuzziness for the belongingness of SAR image pixel makes fuzzy TMF model be able to retain more information from SAR image. ![]() (3) A fuzzy objective function with regularization by Kullback–Leibler information of fuzzy TMF model is constructed for SAR image segmentation. (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. 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. 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. However, TMF model does not take into account the inherent imprecision associated with SAR images. For synthetic aperture radar (SAR) image segmentation, TMF model can adopt diverse statistical models for SAR data related to diverse radar backscattering sources. Triplet Markov fields (TMF) model proposed recently is suitable for nonstationary image segmentation. ![]()
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