Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion
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 it...
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/7/1308 |
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| author | Abdolraheem Khader Jingxiang Yang Sara Abdelwahab Ghorashi Ali Ahmed Zeinab Dehghan Liang Xiao |
| author_facet | Abdolraheem Khader Jingxiang Yang Sara Abdelwahab Ghorashi Ali Ahmed Zeinab Dehghan Liang Xiao |
| author_sort | Abdolraheem Khader |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dbba8e1a586c4520ab488019eca1e83b |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-dbba8e1a586c4520ab488019eca1e83b2025-08-20T03:03:21ZengMDPI AGRemote Sensing2072-42922025-04-01177130810.3390/rs17071308Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image FusionAbdolraheem Khader0Jingxiang Yang1Sara Abdelwahab Ghorashi2Ali Ahmed3Zeinab Dehghan4Liang Xiao5School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaHyperspectral (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.https://www.mdpi.com/2072-4292/17/7/1308global spectral correlationtransformerssuper-resolutionimage restorationattention mechanism |
| spellingShingle | Abdolraheem Khader Jingxiang Yang Sara Abdelwahab Ghorashi Ali Ahmed Zeinab Dehghan Liang Xiao Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion Remote Sensing global spectral correlation transformers super-resolution image restoration attention mechanism |
| title | Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion |
| title_full | Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion |
| title_fullStr | Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion |
| title_full_unstemmed | Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion |
| title_short | Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion |
| title_sort | extensive feature inferring deep network for hyperspectral and multispectral image fusion |
| topic | global spectral correlation transformers super-resolution image restoration attention mechanism |
| url | https://www.mdpi.com/2072-4292/17/7/1308 |
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