Hyperspectral image (HSI) and Multispectral Image (MSI) fusion aims at combining a high-resolution MSI (HR MSI) with a low-resolution HSI (LR HSI), resulting in a fused image that contains the spatial resolution of the former and the spectral resolution of the latter. This approach offers a cost-effective alternative to directly acquiring high-resolution HSIs (HR HSIs). In this survey, we offer an extensive literature review tailored for students and professionals seeking deeper insights into the subject matter. We delve into existing HSI-MSI fusion methods and revealed a spectrum of approaches, ranging from model-driven techniques (extended CS and MRA, Bayesian, matrix factorization, and tensor representation) to data-driven methods (CNN, GAN, and Transformer) and model-data-driven approaches (model-guided networks and semi-supervised or unsupervised methods). This exploration aims to optimize fusion strategies for various applications. This paper not only provides a comprehensive overview of HSI-MSI fusion strategies, but also summarizes and contrasts their unique characteristics, benefits, and limitations. Additionally, it reviews image quality evaluation indices (both full-reference and no-reference) and widely used datasets. Furthermore, using hybrid data, large-view-field satellite data and real satellite data pairs, the reduced-resolution and full-resolution experimental comparison analysis of various algorithms from three strategies are carried out. Finally, the paper identifies unresolved challenges and outlines potential future research directions in this evolving field.
Yan, H-F, Zhao, Y-Q, Chan, JC-W, Kong, SG, El-Bendary, N & Reda, M 2025, 'Hyperspectral and multispectral image fusion: When model-driven meet data-driven strategies', Information Fusion, vol. 116, 102803, pp. 1-24. https://doi.org/10.1016/j.inffus.2024.102803
Yan, H.-F., Zhao, Y.-Q., Chan, J. C.-W., Kong, S. G., El-Bendary, N., & Reda, M. (2025). Hyperspectral and multispectral image fusion: When model-driven meet data-driven strategies. Information Fusion, 116, 1-24. Article 102803. https://doi.org/10.1016/j.inffus.2024.102803
@article{13452c0b8942475a86cd94d02c33d21e,
title = "Hyperspectral and multispectral image fusion: When model-driven meet data-driven strategies",
abstract = "Hyperspectral image (HSI) and Multispectral Image (MSI) fusion aims at combining a high-resolution MSI (HR MSI) with a low-resolution HSI (LR HSI), resulting in a fused image that contains the spatial resolution of the former and the spectral resolution of the latter. This approach offers a cost-effective alternative to directly acquiring high-resolution HSIs (HR HSIs). In this survey, we offer an extensive literature review tailored for students and professionals seeking deeper insights into the subject matter. We delve into existing HSI-MSI fusion methods and revealed a spectrum of approaches, ranging from model-driven techniques (extended CS and MRA, Bayesian, matrix factorization, and tensor representation) to data-driven methods (CNN, GAN, and Transformer) and model-data-driven approaches (model-guided networks and semi-supervised or unsupervised methods). This exploration aims to optimize fusion strategies for various applications. This paper not only provides a comprehensive overview of HSI-MSI fusion strategies, but also summarizes and contrasts their unique characteristics, benefits, and limitations. Additionally, it reviews image quality evaluation indices (both full-reference and no-reference) and widely used datasets. Furthermore, using hybrid data, large-view-field satellite data and real satellite data pairs, the reduced-resolution and full-resolution experimental comparison analysis of various algorithms from three strategies are carried out. Finally, the paper identifies unresolved challenges and outlines potential future research directions in this evolving field.",
author = "Hao-Fang Yan and Yong-Qiang Zhao and Chan, \{Jonathan Cheung-Wai\} and Kong, \{Seong G.\} and Nashwa El-Bendary and Mohamed Reda",
note = "Publisher Copyright: {\textcopyright} 2024",
year = "2025",
month = apr,
doi = "10.1016/j.inffus.2024.102803",
language = "English",
volume = "116",
pages = "1--24",
journal = "Information Fusion",
issn = "1566-2535",
publisher = "Elsevier",
}