Unified Framework for the Joint Super-Resolution and Registration of Multiangle Multi/Hyperspectral Remote Sensing Images

Abstract

In this article, a unified framework based on rank minimization (UFRM) is proposed for use with multiangle multi/hyperspectral remote sensing images, which simultaneously integrates image super-resolution reconstruction (SRR) and image registration. With the complementary information of different angle images and the high correlation between each band of the multi/hyperspectral images, a new image observation model is established to describe the mathematical degradation process of the observed low-resolution multiangle multi/hyperspectral images from the desired high-resolution (HR) multi/hyperspectral image. Based on the rank-one structure of the multiangle images, each observed image is decomposed into a foreground image for each angle image, and a background image, which is shared among all the multiangle images. A multichannel total variation constraint is applied to the target HR background image, with the consideration of the high correlation of different bands. Finally, an alternating minimization optimization strategy is utilized to resolve the joint cost function, which consists of the unknown image registration transformation parameters and the desired reconstruction image. As a result, the UFRM method can simultaneously achieve image registration and SRR. A number of experiments were conducted, which confirmed the superior performance of the proposed method.