Hyperspectral super-resolution with coupled Tucker approximation: Identifiability and SVD-based algorithms

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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https://hal.archives-ouvertes.fr/hal-01911969
Contributor : Konstantin Usevich <>
Submitted on : Monday, November 19, 2018 - 3:08:07 PM
Last modification on : Friday, December 7, 2018 - 5:54:58 PM
Document(s) archivé(s) le : Wednesday, February 20, 2019 - 3:23:14 PM

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  • HAL Id : hal-01911969, version 2
  • ARXIV : 1811.11091

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Clémence Prévost, Konstantin Usevich, Pierre Comon, David Brie. Hyperspectral super-resolution with coupled Tucker approximation: Identifiability and SVD-based algorithms. 2018. ⟨hal-01911969v2⟩

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