Registration of remote-sensing images using robust weighted kernel principal component analysis

Authors: Xifa Duan, Zheng Tian, Mingtao Ding, Wei Zhao

Abstract: 
For pre- and post-earthquake remote-sensing images, registration is a challenging task due to the possible deformations of the objects to be registered. To overcome this problem, a registration method based on robust weighted kernel principal component analysis is proposed to precisely register the variform objects. Firstly, a robust weighted kernel principal component analysis (RWKPCA) method is developed to capture the common robust kernel principal components (RKPCs) of the variform objects. Secondly, a registration approach is derived from the projection on RKPCs. Finally, two experiments are conducted on the SAR image registration in Wenchuan earthquake on May 12, 2008, and the results showed that the method is very effective in capturing structure patterns and generalized well for registration.

Keywords:
Remote-sensing image
Variform object
Kernel principal component analysis (KPCA)
Robust weighted KPCA
Graph spectral method
Outliers

Published in: AEÜ-International Journal of Electronics and Communications (Volume 67, Issue 1, January 2013)

Publisher: Elsevier

ISSN Information: 1434-8411

Registration of remote-sensing images using robust weighted kernel principal component analysis

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