Comparison of radar image segmentation by Gaussian- and Gamma-Markov random field models

Abstract

This paper compares segmentation results of synthetic aperture radar (SAR) images using Gaussian-Markov random field (MRF) and Gamma-MRF models. A Gamma distribution function is more accurate and proper to trace the multilook SAR intensity data distribution. However, it is found that, at least from examples used in the paper, when the distribution function is incorporated with the MRF model to implement SAR image segmentation, the Gamma-MRF model is not necessarily shown to be superior to the Gaussian-MRF model. Occasionally the Gamma-MRF model wrongly merges a few small segments, suggesting that the Gaussian-MRF model might be more stable and reliable.