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

Clémence Prévost (1) , Konstantin Usevich (1) , Pierre Comon (2) , David Brie (1)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 GIPSA-CICS - GIPSA - Communication Information and Complex Systems

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.

Mots clés: hyperspectral super-resolution; low-rank tensor approximation; data fusion; recovery; identifiability

Domaines: Traitement du signal et de l'image [eess.SP]; Théorie de l'information [cs.IT]

Clémence Prévost, Konstantin Usevich, Pierre Comon, David Brie. Hyperspectral Super-Resolution with Coupled Tucker Approximation: Identifiability and SVD-based algorithms. 2018. ⟨hal-01911969v1⟩