Unsupervised Spectral Super-Resolution Guided by Spectral Sampling Priors
Spectral super-resolution (SSR) has garnered significant attention in recent years. Most existing networks rely on supervised methods, which require paired RGB and hyperspectral images (HSIs) for training. However, HSI acquisition is costly and time-consuming due to specialized hardware and complex...
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| Format: | Article |
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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/11098941/ |
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| author | Xintao Zhong Shenfu Zhang Chenyang Lu Xuejian Sun Feng Shao Weiwe Sun Xiangchao Meng |
| author_facet | Xintao Zhong Shenfu Zhang Chenyang Lu Xuejian Sun Feng Shao Weiwe Sun Xiangchao Meng |
| author_sort | Xintao Zhong |
| collection | DOAJ |
| description | Spectral super-resolution (SSR) has garnered significant attention in recent years. Most existing networks rely on supervised methods, which require paired RGB and hyperspectral images (HSIs) for training. However, HSI acquisition is costly and time-consuming due to specialized hardware and complex preprocessing. In addition, spectral mixing phenomena in low-resolution HSIs degrade image quality. To address these challenges, spectral super-resolution (SSR) techniques have emerged to generate high-quality HSIs from widely accessible RGB images, enabling applications in agriculture, medicine, and environmental monitoring. To address these issues, we propose a novel unsupervised SSR network guided by spectral sampling priors (<italic>SPointNet</italic>). Inspired by multimodality text–image fusion techniques, we first introduce the point-image fusion module (PI-Fusion), which fuses sampled spectral data with RGB images. We then utilize spectral unmixing for super-resolution module to produce a coarse HSI, maximizing the exploitation of spectral information. Finally, we integrate a multistage shuffle-unshuffle transformer) to fuse the coarse HSI with the RGB image, enhancing its spatial information. SPointNet can ensure continuity and consistency in both spectral and spatial dimensions in the generation of the refined HSI, which is validated on three publicly available datasets. |
| format | Article |
| id | doaj-art-5d79e446618d4c009bfdb7a43dd9389e |
| institution | Kabale University |
| 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-5d79e446618d4c009bfdb7a43dd9389e2025-08-20T04:03:22ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118197921980410.1109/JSTARS.2025.359366811098941Unsupervised Spectral Super-Resolution Guided by Spectral Sampling PriorsXintao Zhong0https://orcid.org/0009-0007-6795-4932Shenfu Zhang1Chenyang Lu2https://orcid.org/0000-0002-5565-7689Xuejian Sun3Feng Shao4https://orcid.org/0000-0002-2495-9924Weiwe Sun5https://orcid.org/0000-0003-3399-7858Xiangchao Meng6https://orcid.org/0000-0002-7405-3143Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaSpectral super-resolution (SSR) has garnered significant attention in recent years. Most existing networks rely on supervised methods, which require paired RGB and hyperspectral images (HSIs) for training. However, HSI acquisition is costly and time-consuming due to specialized hardware and complex preprocessing. In addition, spectral mixing phenomena in low-resolution HSIs degrade image quality. To address these challenges, spectral super-resolution (SSR) techniques have emerged to generate high-quality HSIs from widely accessible RGB images, enabling applications in agriculture, medicine, and environmental monitoring. To address these issues, we propose a novel unsupervised SSR network guided by spectral sampling priors (<italic>SPointNet</italic>). Inspired by multimodality text–image fusion techniques, we first introduce the point-image fusion module (PI-Fusion), which fuses sampled spectral data with RGB images. We then utilize spectral unmixing for super-resolution module to produce a coarse HSI, maximizing the exploitation of spectral information. Finally, we integrate a multistage shuffle-unshuffle transformer) to fuse the coarse HSI with the RGB image, enhancing its spatial information. SPointNet can ensure continuity and consistency in both spectral and spatial dimensions in the generation of the refined HSI, which is validated on three publicly available datasets.https://ieeexplore.ieee.org/document/11098941/Attention mechanismdeep learninghyperspectral image (HSI)spectral super-resolution (SSR) |
| spellingShingle | Xintao Zhong Shenfu Zhang Chenyang Lu Xuejian Sun Feng Shao Weiwe Sun Xiangchao Meng Unsupervised Spectral Super-Resolution Guided by Spectral Sampling Priors IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Attention mechanism deep learning hyperspectral image (HSI) spectral super-resolution (SSR) |
| title | Unsupervised Spectral Super-Resolution Guided by Spectral Sampling Priors |
| title_full | Unsupervised Spectral Super-Resolution Guided by Spectral Sampling Priors |
| title_fullStr | Unsupervised Spectral Super-Resolution Guided by Spectral Sampling Priors |
| title_full_unstemmed | Unsupervised Spectral Super-Resolution Guided by Spectral Sampling Priors |
| title_short | Unsupervised Spectral Super-Resolution Guided by Spectral Sampling Priors |
| title_sort | unsupervised spectral super resolution guided by spectral sampling priors |
| topic | Attention mechanism deep learning hyperspectral image (HSI) spectral super-resolution (SSR) |
| url | https://ieeexplore.ieee.org/document/11098941/ |
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