期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 68, 期 -, 页码 931-946出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2020.2965305
关键词
Hyperspectral super-resolution; low-rank tensor approximation; data fusion; recovery; identifiability
资金
- ANR(AgenceNationale de Recherche) [OPTIFIN ANR-15-CE10-0007, LeaFleT ANR-19-CE23-0021]
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.
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