改进独立分量分析极化SAR影像相干滤波  

Speckle filtering in polarimetric SAR images based on improved independent component analysis

作　　者：刘宇迪[1] 余洁[1,2,3] 刘利敏[1] 朱腾[1] 

机构地区：[1]武汉大学遥感信息工程学院,武汉430079 [2]首都师范大学资源环境与地理信息系统北京市重点实验室,北京100048 [3]首都师范大学资源环境与旅游学院,北京100048

出　　处：《测绘科学》2015年第10期84-89,共6页Science of Surveying and Mapping

基　　金：863重点项目(2011AA120404);北京市教科研基础建设项目

摘　　要：极化合成孔径雷达(SAR)影像的相干斑滤波,对其后处理起着至关重要的作用。针对传统的独立分量分析(ICA)算法稳定性差、易陷入局部最优等缺陷,该文提出基于粒子群优化(PSO)改进的ICA滤波算法,用于进行极化SAR影像相干斑滤波。该方法利用PSO算法来改进解混矩阵W的行向量的生成过程,以提高ICA算法的分离效果。采用AIRSAR获取的旧金山海湾地区的L波段极化SAR影像进行实验,并用相干斑指数、迭代次数、收敛时间等指标进行客观评价。结果表明,基于PSO改进的ICA算法具有更高的收敛效率和更好的相干斑抑制性能,能有效地降低影像的相干斑噪声。The speckle filtering in polarimetric SAR ing. For that the independent component analysis （ICA）is images is very important to Its post-process- unstable and easily falls into the local optimum, an improved independent component analysis based on particle swarm optimization （PSO）algorithm was proposed in this paper to reduce the polarimetric SAR image speckle. The algorithm focused on improving the generation process of the un-mixing matrix W by PSO algorithm, and improving the separation effi- ciency of the original ICA algorithm. In order to verify the validity of the improved ICA method, the L- band data of the San Francisco bay were tested, which were obtained by AIRSAR. The efficiency was evalua- ted by the speckle reduction index, iterations and convergence time. The experiment results showed that the im- proved ICA algorithm based on PSO had higher convergence speed and better speckle reducing efficiency.

关 键 词：独立分量分析 粒子群算法 极化SAR 相干斑 

分 类 号：P208[天文地球—地图制图学与地理信息工程]