Fine Registration for VHR Images Based on Superpixel Registration-Noise Estimation

Abstract

Local nonlinear geometric distortion is problematic in the registration of very high-resolution (VHR) images. In the standard registration approach, the precision of control points generated from salient feature matching cannot be guaranteed. This letter introduces a novel superpixel registration-noise (RN) estimation method based on a two-step fine registration technique that can be estimate and mitigate the local residual misalignments in VHR images. The first step employs superpixel sparse representation and multiple displacement analysis to estimate RN information of the preregistered image. The second step optimizes the control points obtained in preregistration by combining the RN information and gross error information, and finally fine registers the input image by employing local rectification. The experiments using two data sets generated from Chinese GF2, GF1, and ZY3 satellites are discussed in this letter, and the promising results verify the effectiveness of the proposed new method.