基于灰度共生矩陣技術(shù)，研究了可用于合成孔徑雷達(dá)圖像分類的灰度共生矩陣中差方差、差熵、對(duì)比度、能量、方差等紋理特征量，分析了其特征提取和分類特性。運(yùn)用類內(nèi)類間距準(zhǔn)則，通過(guò)計(jì)算圖像特征值的類內(nèi)類間距，得到對(duì)合成孔徑雷達(dá)圖像分辨效果較好的紋理特征量，并利用三層BP神經(jīng)網(wǎng)絡(luò)進(jìn)行圖像分類，獲得了滿意的分類結(jié)果。
關(guān) 鍵 詞 合成孔徑雷達(dá); 紋理分析; 灰度共生矩陣; 特征提取; 神經(jīng)網(wǎng)絡(luò)

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