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arXiv · 1811.11091

Hyperspectral Super-Resolution with Coupled Tucker Approximation: Recoverability and SVD-based algorithms

Clémence Prévost·Konstantin Usevich·Pierre Comon·David Brie
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Abstract

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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Clémence Prévost, Konstantin Usevich, Pierre Comon, David Brie. 2020-01-20. Hyperspectral Super-Resolution with Coupled Tucker Approximation: Recoverability and SVD-based algorithms. https://doi.org/10.1109/tsp.2020.2965305

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