ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 471-475, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-471-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
14 Sep 2017
REGIONAL SAR IMAGE SEGMENTATION BASED ON FUZZY CLUSTERING WITH GAMMA MIXTURE MODEL
X. L. Li, Q. H. Zhao, and Y. Li Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin, Liaoning 123000, China
Keywords: Synthetic Aperture Radar (SAR), Image segmentation, Gamma mixture model (GaMM), Fuzzy clustering, Markov Random Field (MRF) Abstract. 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.
Conference paper (PDF, 1552 KB)


Citation: Li, X. L., Zhao, Q. H., and Li, Y.: REGIONAL SAR IMAGE SEGMENTATION BASED ON FUZZY CLUSTERING WITH GAMMA MIXTURE MODEL, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 471-475, https://doi.org/10.5194/isprs-annals-IV-2-W4-471-2017, 2017.

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