Synthetic aperture radar image segmentation using fuzzy label field‐based triplet Markov fields model. Issue 12 (1st December 2014)

• Record Type: Journal Article
• Authors: Wang, Fan; Wu, Yan; Fan, Jianwei; Zhang, Xue; Zhang, Qiang; Li, Ming
• Is Part Of: IET image processing. Volume 8:Issue 12(2014)
• Page Start: 856
• Page End: 865
• Publication Date: 2014-12-01
• Subjects: image segmentation -- synthetic aperture radar -- radar imaging -- fuzzy set theory -- Markov processes -- speckle -- iterative methods -- maximum likelihood estimation; synthetic aperture radar image segmentation -- fuzzy label field‐based triplet Markov field model -- nonstationary image segmentation -- multiplicative speckle noise -- fuzzy label field‐based TMF model -- SAR images -- finite discrete set -- fuzzy energy function -- joint prior distribution -- neighbouring pixel detection -- iterative conditional estimation method -- maximum posterior mode criterion -- parameter estimation; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2013.0686
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The recently proposed triplet Markov random fields (TMF) model is very suitable for dealing with non‐stationary image segmentation. However, influenced by multiplicative speckle noise, synthetic aperture radar image (SAR) is dim and blurred in the boundaries of different areas, making it difficult to locate boundary accurately in the segmentation process. Thus, in this study, the authors propose a new segmentation algorithm using fuzzy label field‐based TMF model for SAR images. In the proposed algorithm, the value of each site in the label field is extended from a finite discrete set in the classical TMF model to a continuous one, in order to describe the memberships of each pixel to different classes. A fuzzy energy function is constructed to describe the joint prior distribution of the fuzzy label field and the auxiliary field. The construction of fuzzy energy function also takes into account four direction information and degree of difference between neighbouring pixels. Iterative conditional estimation method and maximum posterior mode criterion are applied to implement parameter estimation and segmentation. Experimental results on simulated data and real SAR images demonstrate the effectiveness of the proposed algorithm.