Fast Fusion of Hyperspectral and Multispectral Images: A Tucker Approximation Approach

Hyperspectral super-resolution based on coupled Tucker decomposition has been recently considered in the remote sensing community. The state-of-the-art approaches did not fully exploit the coupling of information contained in hyperspectral and multispectral images of the same scene. This paper propo...

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Bibliographic Details
Main Authors: Prevost, C., Chainais, P., Boyer, R.
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Computational modeling
Couplings
Image color analysis
Lakes
Runtime
Superresolution
Online Access:Request full text
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Summary:Hyperspectral super-resolution based on coupled Tucker decomposition has been recently considered in the remote sensing community. The state-of-the-art approaches did not fully exploit the coupling of information contained in hyperspectral and multispectral images of the same scene. This paper proposes a new algorithm that overcomes this limitation. It accounts for both the high-resolution and the low-resolution information in the model by solving a set of least-squares problems. In addition, we provide exact recovery conditions for the super-resolution image in the noiseless case. Our simulations show that the proposed algorithm achieves very good reconstruction quality with a very low computational complexity.
ISSN:2381-8549
DOI:10.1109/ICIP46576.2022.9898065