基于MRF的高分辨率SAR图像道路网自动提取

摘要

It is extremely difficult to extract road networks from high resolution synthetic aperture radar (SAR) images due to the presence of various disturbances.Markov random field (MRF) model can make full use of the imagery contextual characters and priori knowledge,which have been widely used to extract road networks.However,there exist some problems such as slow solution and many parameters setting of these type methods.In order to reduce the computation of subsequent iterative solution of MRF,pre-linking is firstly introduced to remove numerous false line elements based on the spatial relationship among them.Then the improved road networks Markov function model is established to label road networks.SAR images with 1 meter resolution are tested in the experiment.The results show the effectivity of the method mentioned above in high resolution SAR imagery road network extraction.%各种干扰的存在使得高分辨率合成孔径雷达(synthetic aperture radar,SAR)图像道路网的提取变得异常困难.马尔可夫随机场(Markov random field,MRF)模型能够充分利用道路图像的上下文特征以及先验知识,在道路网提取中得到广泛应用,但存在求解过程偏慢及参数设置偏多问题.首先根据道路空间几何特征关系对提取出的线基元进行预连接,以此减少虚假连接给MRF迭代求解带来的运算量；然后建立MRF道路网改进模型对道路网进行快速标记.使用1m机载高分辨率SAR图像进行实验,结果验证了该方法的有效性.