We propose a novel approach for hyperspectral super-resolution, that is based on low-rank tensor approximation for a coupled low-rank multilinear (Tucker) model. We show that the correct recovery holds for a wide range of multilinear ranks. For coupled tensor approximation, we propose two SVD-based algorithms that are simple and fast, but with a performance comparable to the state-of-the-art methods. The approach is applicable to the case of unknown spatial degradation and to the pansharpening problem.
@article{arxiv.1811.11091,
title = {Hyperspectral Super-Resolution with Coupled Tucker Approximation: Recoverability and SVD-based algorithms},
author = {Clémence Prévost and Konstantin Usevich and Pierre Comon and David Brie},
journal= {arXiv preprint arXiv:1811.11091},
year = {2020}
}
Comments
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, in Press