Abstract
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.
Highlights
Synthetic Aperture Radar (SAR) (Bombrun et al, 2011) as an important technique of remote sensing is widely used in many fields
In order to solve the problems, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model (VT-GaMM-FCM) is proposed
One is Gamma-FCM which is defined with Gamma distribution, another is GaMM-FCM which is defined with GaMM, both of them are pixel-based
Summary
Synthetic Aperture Radar (SAR) (Bombrun et al, 2011) as an important technique of remote sensing is widely used in many fields. Chatzis and Varvarigou (2008) proposed hidden Markov random field FCM (HMRF-FCM), it defines the dissimilarity measure by Gaussian distribution in fuzzy clusters, and the prior probability is defined by HMRF to consider the effects of neighbor pixels. Considering the speckle noise in SAR image, Gamma distribution is often adopted to depict the stochastic characteristic of pixels (Lopes et al, 1990). The algorithms talked above are all pixels-based, which still can not overcome the speckle noise well in SAR image segmentation. In order to solve the problems, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model (VT-GaMM-FCM) is proposed. After establishing objective function extended from HMRF-FCM, the The optimal segmentation results can be found
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