SAR图像中目标配准的稳健加权核主成分分析方法

针对震前震后合成孔径雷达(SAR)图像中发生复 杂形变的目标,提 出了基于稳健的加权核主成分分析(KPCA)的配准方法。首先,提出 一种稳健的 加权KPCA(RWKPCA)方法,不仅能获得震前震后形变目标的共同稳健核主成分(RKPC s),还可 以作为异常值判别准则；其次,利用在共同RKPCs上的投影定义震前震后形变目标特 征的相似性度 量；最后,利用特征的相似性度量精确配准形变目标。对2008年5月12日汶川地震前后的S AR图像进行配准并与现有方法进行比较,结果表明,本文方法能够有效的得到形变目标的 共同RKPCs,并得到很好的配准结果。

Object registration for SAR images using robust weighted kernel principal component analysis

The registration of pre- and post-earthquake synthetic aperture radar (SAR) images is a challenging problem.The difficulty lies in that the variform objects to be registered often have complex deformations.To solve this problem,this paper proposes a new registration approach based on rob ust weighted kernel principal component analysis (KPCA).We show how the variform objects of pre- and post-ea rthquake can be precisely registered using their robust kernel principal components (RKPCs).The contribut ion can be divided into three parts.Firstly,a robust weighted KPCA (RWKPCA) method is developed,which can n ot only capture the common RKPCs of the variform objects of pre- and post-earthquake,but also act as the criterion for outlier detection. Secondly,based on the projections on common RKPCs,the similarity measure of th e features of the variform objects of pre- and post-earthquake is defined,and thus the matching results c an be obtained.Finally,a variform objects registration approach is derived from the defined similarity measure and the matching results.Two experiments are conducted on the SAR image registration in Wenchuan earthquake, and the results show that compared with the existing methods,our method is more effective in capturing th e common RKPCs of the variform objects of pre- and post-earthquake,and thus ha s a better registration result.