PolSAR图像的改进非局部均值滤波算法  

Improved non-local mean filtering method for PolSAR images

作　　者：韩萍[1] 贾锟 卢晓光[1] 韩宾宾 HAN Ping;JIA Kun;LU Xiaoguang;HAN Binbin(Tianjin Key Lab for Intelligent Signal and Image Processing, Civil AviationUniversity of China, Tianjin 300300, China)

机构地区：[1]中国民航大学天津市智能信号与图像处理重点实验室,天津300300

出　　处：《系统工程与电子技术》2019年第5期992-999,共8页Systems Engineering and Electronics

基　　金：国家自然科学基金(61571442)资助课题

摘　　要：为了在抑制相干斑的同时更好地保留地物目标的极化散射信息和结构信息,提出了一种基于变异系数(coefficient of variance,C.V)的极化合成孔径雷达(polarimetric synthetic aperture radar,PolSAR)图像自适应非局部均值滤波算法。该算法结合图像子块的统计特性和目标点的极化散射特性筛选同质像素,然后引入C.V自适应选取平滑系数来计算滤波所用的权重,最后对同质像素进行非局部均值滤波。用不同系统采集的PolSAR数据进行的实验结果表明,与精致LEE滤波、NL-Pretest滤波以及最新的滤波方法相比,本文算法不仅能够有效抑制相干斑噪声,而且图像的边缘和极化散射特性也得到了更好地保持。In order to better preserve the structural information and polarization scattering information while suppressing speckle, an adaptive non-local mean filtering algorithm based on coefficient of variance (C.V) is proposed. The algorithm combines the statistical properties of the image sub-blocks with the polarization scattering characteristics of the target points to filter the homogeneous pixels, then introduces the C.V adaptive selection smoothing coefficients to calculate the weights used for filtering, and finally performs non-local mean filtering on the homogeneous pixels. The experimental results of polarimetric synthetic aperture radar data collected by different systems show that compared with the refined LEE filter, NL-Pretest filter and the latest filter, the proposed algorithm can effectively remove the speckle noise, and the edge and polarization scattering characteristics of the image are better maintained.

关 键 词：极化合成孔径雷达图像 非局部均值滤波 变异系数 极化散射特性 同质像素筛选 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]