基于SAR 图像的杂波统计特性, 利用广义Gamma模型对降噪配准后的SAR图像统计特征进行拟合, 获取了辐射值与局部纹理等特征信息; 采用信息论中交叉熵的概念, 量化不同时相SAR图像统计特征间的差异程度; 利用KS与KL检验相结合, 自动选取对差异图拟合情况最好的模型, 从而实现基于该模型的KI阈值分割。通过对天津市北辰区以南地区的两幅Radarsat图像, 以及北京市顺义区的两幅ASAR图像的实验表明, 所提出的方法不仅有效地避免了水面波纹变化所产生的大量虚警, 并能有效地检测出传统方法所不能识别的, 区域内均值不变, 仅纹理发生变化的情况。

Based on the clutter statistical characteristics of SAR image, this paper takes advantage of the generalized Gamma model to fit the filtered and co-registered SAR images, in order to gain the characteristics information, such as radiation value, local texture, etc. Then, the degree of evolution between the statistical characteristics of multi temporal SAR image is measured by the definition of Kullback–Leibler Divergence in information theory. Afterwards, a combination of KS and KL test has been applied into the evaluation of fitting function for the difference map captured in the former step, which help select the best fitting function automatically for the model-based KI threshold segmentation. Experiment was carried on the multi temporal SAR im-ages for Southern Part of Tianjin, acquired by Radarsat-1/2, as well as Shunyi District of Beijing, acquired by Envisat-ASAR. Such results confirmed the method proposed in this paper not only avoid large number of false alarms generated from the changes of surface corrugation, but also effectively detected the regions ignored by traditional methods, which have no variance in mean value, but differ in texture.