基于多级分类的极化SAR图像斑点抑制

针对极化合成孔径雷达（polarimetric synthetic aperture radar, PolSAR）图像相干斑抑制后的目标极化特性和结构特征保持问题，给出了一种多级分类的极化SAR图像斑点抑制方法。首先利用H/α快速分解法并结合极化总功率图像进行初分类，之后采用最小距离准则和聚合的层次聚类方法进行细分类，最后根据图像结构将图像内容分为亮点线目标、暗线目标和其他目标三大类，利用线性最小均方滤波器对暗线目标和非点线目标进行滤波。采用美国AIRSAR机载系统获取的实测数据进行实验，结果表明，与Lee的基于散射模型降斑算法相比，本文算法不仅能够更有效地抑制斑点噪声，而且在保持极化特性、结构和纹理特征方面更为有效。

PolSAR image speckle reduction based on multi-stage classification

On account of the scattering properties and target structure feature maintaining in polarimetric synthetic aperture radar(PolSAR) images after speckle suppressing,an effective algorithm for speckle reduction based on multi-stage classification is developed.First,the fast alternative to H/α is used in PolSAR image decomposition and the total back-scattering power is combined for initial pixel classification.Then minimum distance measure and hierarchical cluster method are used in the second stage classification.Finally,all pixels in the image are divided into three classes which are bright point or curve-linear targets,dark curve-linear targets,and other targets.The second and third kinds of targets are filtered by the linear minimize mean square filter(MMSE).Experimental results with AIRSAR data show that the new algorithm is more effective than Scattering-Model-Based speckle filter developed by Lee not only in speckle reduction but also in polarimetric properties and structure feature preservation.