Registration for Variform Object of Remote-Sensing Image Using Improved Robust Weighted Kernel Principal Component Analysis

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, in a previous paper, we proposed a registration method based on robust weighted kernel principal component analysis (RWKPCA) to precisely register the variform objects. Which was proved very effective in capturing the common robust kernel principal components (RKPCs) and generalized well for registration. Compared with previous paper, there are two improvements in this paper: Firstly, we developed the improved RWKPCA method from the robust loss function, and theoretically proved the robustness of the method; Secondly, a new construction of weight function by projection residual was given, which enables the great reduction of computing time. Finally, two experiments were conducted on the remote-sensing image registration in Wenchuan earthquake and change detection of Tangjiashan barrier lake, and the results showed that compared with the previous method, the registration accuracy was increased while the computational time was decreased a lot. Meanwhile, good performance on the change detection of barrier lake is obtained.