Research On Segmentation Methods Based On MRF Model From SAR Images

The aim of Synthetic Aperture Radar (SAR) images segmentation is extracting and detecting specific targets from complex ground scene. The segmentation methods of images based on Markov Random Field (MRF) model able to unify the contextual information from label images and statistical properties of observed images, which are especially suitable for segmentation of SAR images. Therefore, developing a relevant research work on this field is necessary.In this paper, we start our works according to the basic steps of SAR images segmentation based on MRF theory, also concluded and summarized research works and experiences at home and abroad. The work of this paper can be concluded as follows:(1) Discussed the advantages that introduce MRF model to SAR images segmentation, and described the principles and methods of MRF model;(2) Summarized all kinds of existing clutter statistical models based on reading extensive literatures, K distribution model and G~0 distribution model are our focuses;(3) Summarized parameter estimation methods of existing clutter statistical models, introduced G~0 distribution model parameter estimation methods emphatically, then proposed a new parameter estimation method which combines the methods of moments and the method based on Mellin transform on the basis of existing G~0 distribution model parameter estimation methods.(4) Summarized the various types of existing iterative algorithms of MRF model, and analyzed the characteristics of existing iterative algorithms, then some typical algorithms are simulated and compared;(5) Introduced a new SAR images segmentation method with details preserving ability based on adaptive neighborhoods by optimizing the neighborhoods structure of MRF model. We use synthetic and real SAR images to compare the segmentation results, the results show that the segmentation method can preserve image details better.