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Pré-Publication, Document De Travail Année : 2021

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

Résumé

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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Dates et versions

hal-03158076 , version 1 (03-03-2021)
hal-03158076 , version 2 (02-11-2021)

Identifiants

  • HAL Id : hal-03158076 , version 1

Citer

Clémence Prévost, Ricardo A 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-03158076v1⟩
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