一种基于二维Gamma分布的SAR图像变化检测方法

Change detection for SAR images based on bivariate Gamma models

对于隐藏在强杂波环境中的人造目标,传统的恒虚警率(constant false alarm rate,CFAR)目标检测算法受到较大程度的制约。为了改善检测性能,提出了一种基于二维Gamma分布的变化检测算法,并给出了参数估计、变化分析、CFAR归一化、目标聚类等关键步骤的实现方法。该算法在拟合精度较高的二维Gamma分布的基础上,充分利用图像间的相关性抑制强杂波。对实际数据的处理表明,该算法具有较好的检测性能,能在低虚警率的基础上实现较高的检测率。

The traditional CFAR target detection algorithm is strongly restrained for the manmade targets immersed in the environment with strong scattered clutter.In order to improve the detection performance,this paper proposes a novel algorithm based on the bivariate Gamma distributions.In addition,some key steps such as parameter estimation,change analysis,CFAR normalization,and targets clustering are also discussed.This algorithm,based on high approximation accuracy of bivariate Gamma distributions,fully uses the correlation of images to suppress the strong scattered clutter.The results on actual data indicate this algorithm has a quite good detection performance and can realize a relatively high detection rate under the condition of a low false alarm rate.