中文摘要：

像素级的马尔可夫随机场图像分割算法,在强噪声的SAR图像中难以保证消除斑点噪声的同时不破坏图像的边缘结构。针对此,本文提出了一种新的分割方法,首先将图像划分为若干个内部连通且有良好边缘的小区域块,然后将区域块内像素灰度的均值作为块内每个像素的灰度强度以提高算法的抗噪能力。在迭代优化阶段,用区域块替代像素作为新的处理单元,从而减少处理单元的数目提高算法的运行效率。为验证新算法的性能,将其分别应用于人工合成图像和真实的SAR图像上,并与经典分割算法做比较显示出了本文算法在噪声抑制、边缘保护和运算效率方面的性能。

英文摘要：

Image segmentation model based on Markov Random Field （MRF）, which combines prior knowledge and likelihood information, have the certain ability to preserve image boundary and reduce speckle noise. However, the method in SAR image with high noise background is difficult to balance reduction of noise and preservation of boundary because of the abilities based on neighborhood structure. If the neighborhood structure is simple, the algorithm can keep the image boundary, but cannot reduce noise well. Besides, the pixel processing unit in the iterative optimization stage of the method led to massive inefficiencies, and can not meet the requirements of practical application. Thus, a new segmentation strategy is proposed. Firstly, the original image is partitioned into many small segments which have similar grayscale and good boundary. Secondly, in order to enhance the ability of reducing noise of the method, each image pixel gray value is set as the average of the segment to which the pixel belongs. Finally, in iterative optimization stage of the algorithm, each segment in place of pixels was used as the processing units, which reduce the amount of processing unit and improve the efficiency of the algorithm. In order to demonstrate our algorithm＇s performance, simulated and real SAR image＇s segmentation results are presented.