Fisher分布下基于白化滤波的极化SAR图像CFAR检测方法

在已有的极化合成孔径雷达（SAR）图像恒虚警（CFAR）检测方法中，存在着高分辨下杂波模型适用性差的难题.为此提出了一种Fisher分布下具有虚警概率解析表达形式的CFAR检测方法.首先，在乘积模型框架下，引入Fisher纹理变量，推导出了极化白化滤波（PWF）检测量的概率密度函数（PDF）.然后，对PDF积分得到了虚警概率关于检测门限的解析表达形式，并设计了相应的CFAR检测算法流程.最后，通过机载合成孔径雷达（AIRSAR）实测数据比较了新方法和双参数恒虚警（2P-CFAR）算法及已有的基于K分布、G₀分布、Wishart分布的CFAR检测方法的检测性能.结果表明新方法能有效检测出目标，且鲁棒性较强，相比于其他检测方法，品质因数平均高出32.66%.

A New CFAR Detection Method of Polarimetric SAR Imagery Based on Whitening Filter Under Fisher Distribution

There is a problem of the poor applicability of clutter models under high resolution with the existing constant false alarm rate (CFAR) detection methods in polarimetric synthetic aperture radar (SAR) imageries.To solve the problem,a CFAR detection method with an analytical expression for the false alarm rate is proposed.Firstly,the probability density function (PDF) of the polarization whitening filter (PWF) metric is derived based on product model combining the hypothesis of the Fisher distribution texture.Secondly,the PDF of the PWF metric is integrated,and the analytical expression of the false alarm rate with respect to the detection threshold is obtained.The process of the proposed CFAR detection method is also designed.Finally,the CFAR detection performances of the proposed method are compared via airborne SAR (AIRSAR) real data with the existing detection methods,such as the two-parameter CFAR (2P-CFAR) detector and the detection methods based on K distribution,G₀ distribution and Wishart distribution.The results show that the proposed method can detect the targets effectively,and the robustness of the method is strong.Compared with other detection methods,the figure of merit (FoM) of the proposed method is on average higher than 32.66%.