基于MAP估计和广义高斯分布的SAR图像边缘比率检测方法

Ratio-based Edge Detection for SAR Imagery Using MAP Estimation and Generalized Gaussian Distribution

合成孔径雷达(SAR)图像的低信噪比和乘性相干斑噪声给SAR图像的边缘检测带来了极大的困难。通过引入广义高斯(GG)分布作为局部均值功率的先验分布模型,给出了局部均值功率在最大后验概率(MAP)意义下的最优估计,进而提出一种新的SAR图像边缘比率检测算子。利用以梅林变换为基础的对数累积量(MoLC)方法估计GG分布的参数,在此基础上给出一种局部均值功率MAP估计和GG分布参数估计的联合迭代求解方法。利用SAR实测数据对本文提出的边缘检测算子进行仿真验证,并将其与平均比率(RoA)算子和指数加权均值比(ROEWA)算子进行了对比,结果表明该算子可以有效克服相干斑噪声的影响,边缘定位准确且虚假边缘明显减少。

The low signal-to-noise ratio and multiplicative speckle noise make edge detection extremely difficult for synthetic aperture radar（SAR） imagery.In this paper a generalized Gaussian（GG） distribution is used as the a prior model for the local mean power,and an optimal estimation for it in the sense of maximum a posterior（MAP） is thus given.A novel ratio-based edge operator for SAR imagery is accordingly developed.The method of log-cumulants（MoLC） based on Mellin transform is employed to estimate the GG distribution parameters,based on which a method of joint and iterative estimation of the local mean power and GG distribution parameters is proposed.The performance of the proposed edge operator is tested using real SAR data,and compared with the ratio of averages（RoA） and ratio of exponentially weighted averages（ROEWA） operators.Experimental results show that the proposed operator can effectively extract edges and suppress speckle noise.