一种利用Cloude-Pottier分解和极化白化滤波的全极化SAR图像分类算法

提出了一种新的基于Cloude-Pottier分解和极化白化滤波(PWF)的全极化SAR数据分类算法。该算法利用PWF的结果来代替反熵A对复WishartH/α分类结果进行进一步细化,按PWF的值将复WishartH/α分类结果由8类分为16类,然后再次进行Wishart迭代分类。实验结果表明,该算法能有效地提高分类精度,分类结果明显优于常规的复WishartH/α分类结果和复WishartH/α/A分类结果。

An Unsupervised Wishart Classification for Fully Polarimetric SAR Image Based on Cloude-Pottier Decomposition and Polarimetric Whitening Filter

Cloude-Pottier decomposition has been widely used in feature extraction and classification because it can include all scattering mechanism and can ensure the same eigenvalue under different polarimetric bases.A new unsupervised classification method is proposed for fully polarimetric SAR image based on Cloude-Pottier decomposition and polarimetric whitening filter(PWF).The result of PWF is used to improve the classic Wishart H/α classification instead of anisotropy A.As initialization,the Wishart H/α classification result is divided into 16 classes according to the PWF result.Then after another Wishart iteration,the final result would be got.The experimental results show that the PWF result has more information that the H,α and A parameters don’t contain.It is also shown that the proposed classification method provides better performance than the general Wishart H/α classification and Wishart H/α/A classification.