Hyperspectral Super-Resolution Via Coupled Tensor Factorization: Identifiability and Algorithms

Abstract

This work focuses on the problem of fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image that admits high spatial and spectral resolutions. Existing algorithms are mostly based on joint low-rank factorization of the ma-tricized HSI and MSI. This framework is effective to some extent, but several challenges remain. First, it is unclear whether or not the super-resolution image is identifiable in theory under this framework, while identifiability usually plays an essential role in such estimation problems. Second, most algorithms assume that the degradation operators from the super-resolution image to the HSI and MSI are known or can be easily estimated - which is hardly true in practice. In this work, we propose a novel coupled tensor decomposition method that can effectively circumvent these issues. The proposed approach guarantees the identifiability of the super-resolution image under realistic conditions. The method can work even without knowing the spatial degradation operator, which could be hard to accurately estimate in practice. Simulations using AVIRIS Cuprite data are employed to demonstrate the effectiveness of the proposed approach.