Fuzzy Clustering Algorithm for Multi-view SAR Image Segmentation Based on Gamma Distribution with Variable Shape Parameter

Considering the problem that the traditional fuzzy clustering algorithm can not overcome the inherent speckle noise of SAR image, a fuzzy clustering segmentation algorithm using variable shape parameter Gamma distribution and neighborhood correlation is proposed.The variable shape parameter Gamma distribution is used to model the speckle noise of the multi-view SAR intensity image, and its negative logarithm is used as the similarity measure between the pixel and the intensity of the cluster in the feature field.Markov random field (MRF) is used to establish a generic correlation model of neighborhood pixels in label fields.In the framework of fuzzy clustering, the fuzzy objective function is constructed based on the above models, and the optimal result is obtained under the objective function minimization criterion.Experiments show that the variable shape parameter Gamma distribution can more accurately fit the histogram of pixel intensity in the homogeneous region.In order to effectively solve the shape parameter contained in the Gamma function, Newton iteration algorithm is used to estimate its numerical solution.Qualitative and quantitative analysis results show that the algorithm is effective in segmenting synthetic and real multi-view SAR images.