基于改进Markov随机场的高分辨率SAR图像建筑物分割算法

Building Segmentation from High-Resolution SAR Images Based on Improved Markov Random Field

提出了一种基于改进Markov随机场模型的高分辨率SAR(Synthetic Aperture Radar,合成孔径雷达)图像建筑物分割算法.针对高分辨率SAR图像信噪比低和建筑物复杂纹理特性的特点,采用多尺度Markov随机场模型的最大似然准则方法获取图像的初始分割,并在传统Markov邻域能量模型基础之上提出一种新的基于Gabor纹理相似度的邻域势函数模型,采用ICM(Iterative Conditional Model,迭代条件模型)算法进行建筑物分割.多组实际高分辨率SAR图像的实验结果表明,与传统MRF算法等方法相比,本文方法具有更高的分割正确率,同时建筑物边界更为清晰平滑,分割效果较好.

An approach was proposed for building segmentation from high resolution SAR （Synthetic Aperture Radar） im ages based on an improved Markov random field （MRF） model. Aiming at the property of low SNR （Signal to Noise Ratio） of SAR images and the complexity of building textures, we obtained the initial segmentation using the maximum likelihood （NIL） algo rithm based on the multiscale MRF model and involved the Gabor similarity between pixels based on the traditional MRF potential function, and employed the ICM （ Itemtive Conditional Model） algorithm to implement the segmentation. The experimental results on several real SAR images show that the proposed approach performs better than traditional methods in the segmentation accuracy, and building boundaries are clearly obtained by the proposed approach.