基于密度峰值搜索的全极化SAR图像分类

Classification method for POLSAR images based on find of density peak

提出一种基于密度峰值搜索(find of density peaks,FDP)的全极化SAR图像(polarimetric synthetic aperture radar,POLSAR)无监督分类方法。由于在边缘地带以及奇异点的散射类型复杂,在无监督分类过程中干扰巨大,本文通过构建基于信息熵的显著性图来剔除这一类点的影响,并对剩余部分的参数进行了加权处理。随后在H/珔α/A/SPAN空间基于FDP方法进行无监督分类。最后通过ESAR的数据进行了实验验证,结果证明了方法的有效性。

An unsupervised classification method based on find of density peaks（FDP）is proposed for the polarimetric synthetic aperture radar（POLSAR）image.For the great impact of the boundary and strong points in the POLSAR image,the following density becomes unstable.The saliancy image which is based on the information entropy is proposed to remove these points before classification.The feature in H/α^-/A/SPANspace of the remaining pixels is weighted with the saliancy value.Then the unsupervised classification is achieved based on the FDP.In the experiment with the ESAR data,results validate the effectiveness of the new method.