UWB SAR Change Detection of Target in Foliage Based on Local Statistic Distribution Change Analysis
基于邻域统计分布变化分析的UWB SAR隐蔽目标变化检测

Because of large pixel value change between multitemporal UWB SAR images caused by different imaging geometries, the performance of change detection algorithm based on pixel value difference declines quickly. In order to deal with this problem, a new UWB SAR foliage target change detection algorithm based on local statistic distribution is proposed. In the algorithm, the Gram-Charlier expansion theory and rank order filter are combined to estimate local statistic distribution. Then, the K-L divergence is used to measure the change between local statistic distribution of multitemporal UWB SAR image. And the target can be detected because of large K-L divergence value. Finally, the experimental results show that the algorithm can better deal with the pixel value change between multitemporal UWB SAR images with different imaging geometries and an obvious performance improvement on detection can be obtained.