Abstract
This paper presents a Synthetic Aperture Radar (SAR) image segmentation approach with unknown number of classes, which is based on regular tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC') algorithm. First of all, an image domain is portioned into a set of blocks by regular tessellation. The image is modeled on the assumption that intensities of its pixels in each homogeneous region satisfy an identical and independent Gamma distribution. By Bayesian paradigm, the posterior distribution is obtained to build the region-based image segmentation model. Then, a RJMCMC algorithm is designed to simulate from the segmentation model to determine the number of homogeneous regions and segment the image. In order to further improve the segmentation accuracy, a refined operation is performed. To illustrate the feasibility and effectiveness of the proposed approach, two real SAR image is tested.
Highlights
Image segmentation is a hot topic in Synthetic Aperture Radar (SAR) image processing and involves two tasks: determining the number of homogeneous regions and segmenting them
The number of partitioned blocks in the image domain m is considered as a random variable, and has a prior truncated
Segmentation is performed according to Reversible Jump Markov Chain Monte Carlo (RJMCMC) to simulate the posterior distribution defined in Eq (8)
Summary
Image segmentation is a hot topic in Synthetic Aperture Radar (SAR) image processing and involves two tasks: determining the number of homogeneous regions and segmenting them. In order to automatically determine the number of homogeneous regions, Askari et al (2013) presented an approach to SAR image segmentation. The proposed approach couples regular tessellation with RJMCMC algorithm to automatically determine the number of homogeneous regions and segment the image. In the RJMCMC algorithm, five move types are designed, involving splitting or merging real classes, sampling parameter vector, sampling label field, birth or death of an empty class and splitting or merging blocks. These moves overcome the instability problem of segmentation optimization.
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