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Article

Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion

1
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
3
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(7), 1308; https://doi.org/10.3390/rs17071308 (registering DOI)
Submission received: 26 January 2025 / Revised: 28 March 2025 / Accepted: 4 April 2025 / Published: 5 April 2025

Abstract

Hyperspectral (HS) and multispectral (MS) image fusion is the most favorable way to obtain a hyperspectral image that has high resolution in terms of spatial and spectral information. This fusion problem can be accomplished by formulating a mathematical model and solving it either analytically or iteratively. The mathematical solutions class has serious challenges, e.g., computation cost, manually tuning parameters, and the absence of imaging models that laboriously affect the fusion process. With the revolution of deep learning, the recent HS-MS image fusion techniques gained good outcomes by utilizing the power of the convolutional neural network (CNN) for feature extraction. Moreover, extracting intrinsic information, e.g., non-local spatial and global spectral features, is the most critical issue faced by deep learning methods. Therefore, this paper proposes an Extensive Feature-Inferring Deep Network (EFINet) with extensive-scale feature-interacting and global correlation refinement modules to improve the effectiveness of HS-MS image fusion. The proposed network retains the most vital information through the extensive-scale feature-interacting module in various feature scales. Moreover, the global semantic information is achieved by utilizing the global correlation refinement module. The proposed network is validated through rich experiments conducted on two popular datasets, the Houston and Chikusei datasets, and it attains good performance compared to the state-of-the-art HS-MS image fusion techniques.
Keywords: global spectral correlation; transformers; super-resolution; image restoration; attention mechanism global spectral correlation; transformers; super-resolution; image restoration; attention mechanism

Share and Cite

MDPI and ACS Style

Khader, A.; Yang, J.; Ghorashi, S.A.; Ahmed, A.; Dehghan, Z.; Xiao, L. Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion. Remote Sens. 2025, 17, 1308. https://doi.org/10.3390/rs17071308

AMA Style

Khader A, Yang J, Ghorashi SA, Ahmed A, Dehghan Z, Xiao L. Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion. Remote Sensing. 2025; 17(7):1308. https://doi.org/10.3390/rs17071308

Chicago/Turabian Style

Khader, Abdolraheem, Jingxiang Yang, Sara Abdelwahab Ghorashi, Ali Ahmed, Zeinab Dehghan, and Liang Xiao. 2025. "Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion" Remote Sensing 17, no. 7: 1308. https://doi.org/10.3390/rs17071308

APA Style

Khader, A., Yang, J., Ghorashi, S. A., Ahmed, A., Dehghan, Z., & Xiao, L. (2025). Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion. Remote Sensing, 17(7), 1308. https://doi.org/10.3390/rs17071308

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