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 i...

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Veröffentlicht in:	Pattern recognition Jg. 45; H. 11; S. 4018 - 4033
Hauptverfasser:	Zhang, Peng, Li, Ming, Wu, Yan, Gan, Lu, Liu, Ming, Wang, Fan, Liu, Gaofeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.11.2012 Elsevier
Schlagworte:	Applied sciences Detection, estimation, filtering, equalization, prediction Exact sciences and technology Fuzzy Fuzzy clustering Fuzzy iterative conditional estimation Fuzzy logic Fuzzy objective function Fuzzy set theory Fuzzy triplet Markov field (FTMF) Image processing Image segmentation Information, signal and communications theory Mathematical analysis Mathematical models Multi-class segmentation SAR image Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Synthetic aperture radar Telecommunications and information theory Fuzzy clustering Fuzzy iterative conditional estimation Fuzzy triplet Markov field (FTMF) Multi-class segmentation Fuzzy objective function SAR image Performance evaluation Automatic classification Parameter estimation Image processing Statistical distribution Backscattering Iterative method Unsupervised classification Modeling Triplet Markov model Radar imaging Synthetic aperture radar Probabilistic approach Non stationary condition Markov model Prior probability Gibbs sampling Algorithm Signal classification Image segmentation Statistical model Field theory Objective function Planar technology
ISSN:	0031-3203, 1873-5142
Online-Zugang:	Volltext
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