基于Gibbs-Markov随机场与连通聚类的SAR图像恢复

在传统的马尔可夫随机场(MRF)的图像建模方法基础上利用合成孔径雷达(SAR)图像的固有特性对Gibbs-MRF模型进行改进复原SAR图像,并进一步提出用数字形态学中连通性理论进行图像分割。在SAR图像像素空间的邻域内,估计最大后验概率(MAP)时引用Gamma分布代替传统的瑞利分布恢复数据,同时利用像素强度值相关性的连通模型将目标较好地提取出来。充分利用了SAR图像的数字形态信息和像素强度之间的相关性,得到了更好的分割效果。仿真实验说明本文方法是有效的。

SAR Image Restoration Based on Gibbs-Markov Random Field Model and Connected Clustering

This article proposes to make use of the inherent characteristics of synthetic aperture radar(SAR) images to improve the Gibbs-Markov random field(MRF) model for recovering the SAR images.Further,it segments a SAR image into the target and the shadow by means of the theory of connectivity in digital morphology.The new method uses not only Gamma distribution to replace the traditional Rayleigh distribution in the estimate of a maximum a posteriori probability(MAP),but also the connectivity model of pixel intensity value relevance to better extract the goal in the neighborhood of the SAR image pixel space.This method takes full advantage of the relevance between the information of digital morphology of the SAR image and the pixel intensity,and is able to eliminate isolated points and obtain good segment results.Simulation tests illustrate the validity of the method.