Based on the gray level co-occurrence matrix technology, the texture features such as difference variance, difference entropy, contrast, energy, variance in the gray level co-occurrence matrix that can be used for synthetic aperture radar image classification are studied, and their feature extraction and classification characteristics are analyzed. Using the intra-class distance criterion, by calculating the intra-class distance of the image eigenvalues, the texture feature with good resolution effect for synthetic aperture radar images is obtained, and the image classification is carried out using a three-layer BP neural network, and satisfactory classification results are obtained. Keywords synthetic aperture radar; texture analysis; gray-level co-occurrence matrix; feature extraction; neural network 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
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