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Hyperspectral super-resolution accounting for spectral variability: LL1-based recovery and blind unmixing

Abstract : In this paper, we propose to jointly solve the hyperspectral super-resolution and hyperspectral unmixing problems using a coupled LL1 block-tensor decomposition. We focus on the specific case of spectral variability occurring between the observed low-resolution images. Exact recovery conditions are provided. We propose two algorithms: an unconstrained one and another one subject to non-negativity constraints, to solve the problems at hand. We showcase performance of the proposed approach on a set of synthetic and semi-real images.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-03158076
Contributor : Clémence Prévost <>
Submitted on : Wednesday, March 3, 2021 - 3:55:09 PM
Last modification on : Wednesday, March 10, 2021 - 3:34:40 AM

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  • HAL Id : hal-03158076, version 1

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Clémence Prévost, Ricardo Borsoi, Konstantin Usevich, David Brie, José C. M. Bermudez, et al.. Hyperspectral super-resolution accounting for spectral variability: LL1-based recovery and blind unmixing. 2021. ⟨hal-03158076⟩

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