Robust Registration by Rank Minimization for Multiangle Hyper/Multispectral Remotely Sensed Imagery

Abstract

Multiangle images are acquired over roughly the same earth surface from different angles, and accurate image registration is a key prerequisite for their application. In this paper, we propose a robust registration method by rank minimization (RRRM) for multiangle hyper/multispectral remotely sensed imagery (MA-HSI-MSI). First, the low-rank structure of the MA-HSI-MSI is exploited and utilized as the registration constraint, thus recasting the image registration problem as searching for an optimal set of transformations, such that the matrix of the transformed images can reach its minimum rank. Second, a patch-based registration scheme is adopted to solve the problem of inconsistent geometric distortion over the entire image, taking the homography model as the local transformation. An iterative convex optimization algorithm is then used to solve the rank minimization-based image registration model for each image patch. Finally, all the transformed patches are used to synthesize the final registration image. The experimental results demonstrate that the proposed low-rank registration method works effectively for CHRIS/Proba imagery and WorldView-2 imagery.