Dual-Branch Network With Mutual Guidance for Hyperspectral Image Superresolution
Fusion-based hyperspectral image superresolution has recently attracted increasing interest due to its superior reconstruction quality. This approach enhances the spatial resolution of low-resolution hyperspectral images (LR-HSIs) by fusing high-resolution multispectral images (HR-MSIs) of the same...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11002502/ |
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| _version_ | 1850219313363943424 |
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| author | Tiejun Zhang Boqun Zhang Nan Hu Xinyu Li Peng Li |
| author_facet | Tiejun Zhang Boqun Zhang Nan Hu Xinyu Li Peng Li |
| author_sort | Tiejun Zhang |
| collection | DOAJ |
| description | Fusion-based hyperspectral image superresolution has recently attracted increasing interest due to its superior reconstruction quality. This approach enhances the spatial resolution of low-resolution hyperspectral images (LR-HSIs) by fusing high-resolution multispectral images (HR-MSIs) of the same scene. However, most existing deep learning-based methods have not sufficiently considered the huge modality differences between these two types of images before feature fusion, potentially resulting in the loss of valuable information. To tackle this challenge, we introduce a novel dual-branch network with mutual guidance (DBMGNet). Specifically, we employ a dual-branch architecture to process the input LR-HSI and HR-MSI in parallel, with the aim of reconciling their modality differences. For this purpose, we designed a mutually guided dual-stream transformer block that performs bidirectional calibration between the branches, enhancing their spatial and spectral consistency. To prevent excessive coupling of information, we proposed a multiscale feature enhancement block, which independently refines fine-grained details within each branch. Finally, a weighted feature fusion block is developed to effectively integrate the features from both branches. Experiments on three widely used datasets indicate that the proposed DBMGNet achieves stable and superior performance with lower computational cost in comparison with several state-of-the-art approaches. |
| format | Article |
| id | doaj-art-b9f736d4f7d54066b26a518aacd7caac |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-b9f736d4f7d54066b26a518aacd7caac2025-08-20T02:07:24ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118143681438110.1109/JSTARS.2025.356933111002502Dual-Branch Network With Mutual Guidance for Hyperspectral Image SuperresolutionTiejun Zhang0https://orcid.org/0009-0005-9644-2456Boqun Zhang1https://orcid.org/0009-0000-5348-6882Nan Hu2https://orcid.org/0009-0009-7714-6925Xinyu Li3https://orcid.org/0009-0006-7887-2767Peng Li4https://orcid.org/0009-0002-7688-0473College of Computer Science and Software, Harbin University of Science and Technology, Harbin, ChinaCollege of Computer Science and Software, Harbin University of Science and Technology, Harbin, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Software, Harbin University of Science and Technology, Harbin, ChinaCollege of Computer Science and Software, Harbin University of Science and Technology, Harbin, ChinaFusion-based hyperspectral image superresolution has recently attracted increasing interest due to its superior reconstruction quality. This approach enhances the spatial resolution of low-resolution hyperspectral images (LR-HSIs) by fusing high-resolution multispectral images (HR-MSIs) of the same scene. However, most existing deep learning-based methods have not sufficiently considered the huge modality differences between these two types of images before feature fusion, potentially resulting in the loss of valuable information. To tackle this challenge, we introduce a novel dual-branch network with mutual guidance (DBMGNet). Specifically, we employ a dual-branch architecture to process the input LR-HSI and HR-MSI in parallel, with the aim of reconciling their modality differences. For this purpose, we designed a mutually guided dual-stream transformer block that performs bidirectional calibration between the branches, enhancing their spatial and spectral consistency. To prevent excessive coupling of information, we proposed a multiscale feature enhancement block, which independently refines fine-grained details within each branch. Finally, a weighted feature fusion block is developed to effectively integrate the features from both branches. Experiments on three widely used datasets indicate that the proposed DBMGNet achieves stable and superior performance with lower computational cost in comparison with several state-of-the-art approaches.https://ieeexplore.ieee.org/document/11002502/Dual-branch networkhyperspectral image superresolutionimage fusiontransformer |
| spellingShingle | Tiejun Zhang Boqun Zhang Nan Hu Xinyu Li Peng Li Dual-Branch Network With Mutual Guidance for Hyperspectral Image Superresolution IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Dual-branch network hyperspectral image superresolution image fusion transformer |
| title | Dual-Branch Network With Mutual Guidance for Hyperspectral Image Superresolution |
| title_full | Dual-Branch Network With Mutual Guidance for Hyperspectral Image Superresolution |
| title_fullStr | Dual-Branch Network With Mutual Guidance for Hyperspectral Image Superresolution |
| title_full_unstemmed | Dual-Branch Network With Mutual Guidance for Hyperspectral Image Superresolution |
| title_short | Dual-Branch Network With Mutual Guidance for Hyperspectral Image Superresolution |
| title_sort | dual branch network with mutual guidance for hyperspectral image superresolution |
| topic | Dual-branch network hyperspectral image superresolution image fusion transformer |
| url | https://ieeexplore.ieee.org/document/11002502/ |
| work_keys_str_mv | AT tiejunzhang dualbranchnetworkwithmutualguidanceforhyperspectralimagesuperresolution AT boqunzhang dualbranchnetworkwithmutualguidanceforhyperspectralimagesuperresolution AT nanhu dualbranchnetworkwithmutualguidanceforhyperspectralimagesuperresolution AT xinyuli dualbranchnetworkwithmutualguidanceforhyperspectralimagesuperresolution AT pengli dualbranchnetworkwithmutualguidanceforhyperspectralimagesuperresolution |