中文摘要：

该文将传统区域统计分布特征变化检测方法拓展到2维特征空间,提出一种基于2维概率密度函数比较的SAR图像变化检测方法。该方法首先将观测区域内相邻像素的灰度值组合成2维观测矢量,而后采用2维GramCharlier展开式对观测矢量在不同时相图像中的2维概率密度函数分别进行估计,在此基础上,借助K-L散度理论对2维概率密度函数在不同时相图像间的变化大小进行定量分析以实现变化检测。实验结果表明,与传统方法相比,该文方法具有更优的检测性能。

英文摘要：

In this paper, the tradition change detection method based on local statistical feature is expanded to two-dimensional feature space, and a SAR image change detection method based on comparison of two-dimensional probability density functions is proposed. In this method, the values of adjacent pixels are combined to build two-dimensional observation vector. Then, in each temporal image, the Probability Density Function（PDF） of the vector is estimated by two-dimensional Gram-Charlier expansion. On the basis, change detection is fulfilled by computing the K-L divergence between the PDFs in different temporal images. Experiment results show that the proposed algorithm has better performance than the traditional method.