为实现 MSTAR图像无监督分割，并提高分割精度和计算效率，提出了一种基于 Gabor滤波增强的自适应阈值分割算法。首先利用多尺度、多方向的 Gabor滤波器组对待分割图像进行滤波处理，抑制目标、阴影和背景区域内部的斑噪起伏，同时增强区域间的差异性；在此基础上，通过对增强图像统计特性的分析，给出了灰度阈值计算形式，实现了 MSTAR图像的自适应分割。实验结果表明，本文算法对不同斑噪强度的 MSTAR图像均具有良好的处理效果，在分割精度、计算效率等方面优于传统的 OTSU，以及 FCM、MRF等分割方法。

Image segmentation is a hot point in the research field of automatic target recognition of SAR image. In order to segment the MSTAR image automatically, a new adaptive method is proposed. Firstly, 2D Gabor filters with various orientations and scales are used to enhance the original image, which can effectually reduce speckle noise in the background, and smooth the interior of the homogeneous regions. Then the analysis of the statistical characteristics of the enhanced image is made, based on which the adaptive thresholding rules is presented for the automatically segmentation of the images. Experiment results with the MSTAR images indicate that the algorithm presented has advantage of segmentation accuracy, calculation efficiency and noise robustness over the traditional methods, such as OTSU, FCM and MRF.