REGIONAL SAR IMAGE SEGMENTATION BASED ON FUZZY CLUSTERING WITH GAMMA MIXTURE MODEL

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this pap...

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Bibliographic Details
Published inISPRS annals of the photogrammetry, remote sensing and spatial information sciences Vol. IV-2/W4; pp. 471 - 475
Main Authors Li, X. L., Zhao, Q. H., Li, Y.
Format Journal Article
LanguageEnglish
Published Gottingen Copernicus GmbH 14.09.2017
Copernicus Publications
Subjects
Algorithms
Clustering
Clusters
Computer simulation
Image segmentation
Manufacturing cells
Markov chains
Objective function
Pixels
Qualitative analysis
Quantitative analysis
Radar imaging
Stochastic processes
Synthetic aperture radar
Tessellation
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Summary:Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.
ISSN:2194-9050
2194-9042
2194-9050
DOI:10.5194/isprs-annals-IV-2-W4-471-2017