基于灰度共生矩阵技术，研究了可用于合成孔径雷达图像分类的灰度共生矩阵中差方差、差熵、对比度、能量、方差等纹理特征量，分析了其特征提取和分类特性。运用类内类间距准则，通过计算图像特征值的类内类间距，得到对合成孔径雷达图像分辨效果较好的纹理特征量，并利用三层BP神经网络进行图像分类，获得了满意的分类结果。关 键 词 合成孔径雷达; 纹理分析; 灰度共生矩阵; 特征提取; 神经网络Abstract This paper is based on the gray-level co-occurrence matrix method, and particularly study some texture features used for the classification of SAR images, including difference variance 、difference average、difference entropy、contrast、energy、variance、sum variance、inverse difference moment and correlation etc. Furthermore we have abstracted features of SAR image and studied classification characteristic. Using criterion called distance of inside classes and between classes, we can get a few features of a SAR image which is good at image classification. At last, making use of BP neural network of three layers , we proceed image classification and get the satisfied results.Key words synthetic aperture radar; texture features; gray-level co-occurrence matrix; features extraction; neural network