基于去取向理论的全极化SAR图像非监督分类

由于复杂地表目标的取向往往是随机分布的,产生散射回波的随机起伏使散射目标分类不容易明确,采用常规的分类方法容易造成地表的分类混淆.文中提出了一种全极化SAR图像非监督分类方法,首先对数据进行极化去取向处理,提取极化特征参数u、v,结合极化熵参数H进行非监督分类;之后将分类结果作为改进C-均值算法的初始类别划分,基于由u/v/H3个参数组成的特征空间,采用迭代方法实现对地物的分类;最后对NASA/JPL实验室的实测数据进行了实验分析,验证了文中所提分类方法的有效性.

Unsupervised Classification of Fully Polarimetric SAR Image Based on Deorientation Theory

The randomly distributed target orientation causes confusion in classification of the polarimetric scattering target.In this paper,an unsupervised classification method of the fully polarimetric SAR image is proposed to solve this problem.Firstly,a certain angle rotation of the part along the sight line is made to minimize the cross-polarized scattering;then an initial classification using the scattering mechanism is implanted,which is described by the parameters u/v/H.Finally,using the initial classificati...