针对目标监测分析中的SAR图像分割问题，构造了一种基于马尔可夫随机场(MRF)模型和形态学运算的处理方法。首先利用SAR图像邻域空间上的马尔可夫性以及像素灰度的高斯分布模型，以较少的迭代次数实现了SAR图像的初分割; 然后通过形态学运算进行处理，抑制干扰性分割，同时填充目标区域内部空洞，改善分割效果。实验结果显示，该方法可以较好地实现SAR图像目标区域的分割，且处理效率较高，利于实现SAR图像的快速有效分割。

A combined method based on Markov Random Field (MRF) model and morphological operation was presented for the segmentation of the SAR image in target monitoring. Firstly, on the basis of the Markov characteristics in the neighboring region of the SAR image, and the Gaussian distribution model hypothesis of pixel gray-level, the pre-segmented image was gained with much fewer iterative times. And then, the morphological operation was used to restrain the interfering segmentations and to fill the holes in the target regions, which could prominently improve the segmentation effects. The experiment results with high resolution SAR images indicated that this method has a better segmenting capability with high processing efficiency, which is propitious to fast and effective SAR images segmentation.