Paper Info
Title
Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion With Inter-Image Variability
Abstract
Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images, reconciling state of the art performance with strong theoretical guarantees. However, tensor-based approaches previously proposed assume that the different observed images are acquired under exactly the same conditions. A recent work proposed to accommodate inter-image spectral variability in the image fusion problem using a matrix factorization-based formulation, but did not account for spatially-localized variations. Moreover, it lacks theoretical guarantees and has a high associated computational complexity. In this paper, we consider the image fusion problem while accounting for both spatially and spectrally localized changes in an additive model. We first study how the general identifiability of the model is impacted by the presence of such changes. Then, assuming that the high-resolution image and the variation factors admit a Tucker decomposition, two new algorithms are proposed - one purely algebraic, and another based on an optimization procedure. Theoretical guarantees for the exact recovery of the high-resolution image are provided for both algorithms. Experimental results show that the proposed method outperforms state-of-the-art methods in the presence of spectral and spatial variations between the images, at a smaller computational cost.
Year
DOI
Venue
2021
10.1109/JSTSP.2021.3054338
IEEE Journal of Selected Topics in Signal Processing
Keywords
DocType
Volume
Hyperspectral data,image fusion,inter-image variability,multispectral data,super-resolution,tensor decomposition
Journal
15
Issue
ISSN
Citations 
3
1932-4553
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Ricardo Augusto Borsoi1227.60
Clémence Prévost212.03
Konstantin Usevich310914.07
David Brie413024.28
jose c m bermudez516211.12
Cédric Richard694071.61