Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature
Hyperspectral (HS) remote sensing imagery has broad applications but faces challenges in acquiring high-quality wide-swath data due to sensor hardware limitations. To address this, we proposed a jointly fused convolutional neural network for spectral super-resolution (JF-CNNSSR), leveraging spectral...
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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/11045064/ |
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| author | Wang Benlin Yu Qinglin Wang Zuo Li Weitao Wang Yong Liu Huan Gu Shuangxi Zhang Lingling Lv Dong |
| author_facet | Wang Benlin Yu Qinglin Wang Zuo Li Weitao Wang Yong Liu Huan Gu Shuangxi Zhang Lingling Lv Dong |
| author_sort | Wang Benlin |
| collection | DOAJ |
| description | Hyperspectral (HS) remote sensing imagery has broad applications but faces challenges in acquiring high-quality wide-swath data due to sensor hardware limitations. To address this, we proposed a jointly fused convolutional neural network for spectral super-resolution (JF-CNNSSR), leveraging spectral reflectance variations across different land cover types. The method employs paired overlapping multispectral (MS) and HS images to develop two spectral mapping networks: a 1-D-CNN spectral mapping network and a 3-D-CNN spectral mapping network, trained class-specifically on clustered spectral samples. A spectral distance-based joint weighting mechanism integrates outputs from both networks to synthesize HS imagery spatially consistent with the input MS data. Experiments on four benchmark datasets demonstrate that JF-CNNSSR outperforms existing spectral super-resolution methods, achieving significant improvements across key quantitative metrics including peak signal-to-noise ratio, spectral angle mapper, structural similarity index measure, ERGAS, and MSE. Practical validation through grassland coverage estimation in China’s Three-River Source Region further confirms the superiority of reconstructed HS imagery over original MS data in ecological application accuracy. |
| format | Article |
| id | doaj-art-e35757a0cd234086a12448abacfdec0f |
| 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-e35757a0cd234086a12448abacfdec0f2025-08-20T02:36:06ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118162271624510.1109/JSTARS.2025.358133811045064Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral FeatureWang Benlin0https://orcid.org/0000-0001-6677-3813Yu Qinglin1https://orcid.org/0009-0008-9257-4832Wang Zuo2Li Weitao3Wang Yong4Liu Huan5Gu Shuangxi6Zhang Lingling7Lv Dong8School of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaAnhui Normal University, Wuhu, ChinaSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaHyperspectral (HS) remote sensing imagery has broad applications but faces challenges in acquiring high-quality wide-swath data due to sensor hardware limitations. To address this, we proposed a jointly fused convolutional neural network for spectral super-resolution (JF-CNNSSR), leveraging spectral reflectance variations across different land cover types. The method employs paired overlapping multispectral (MS) and HS images to develop two spectral mapping networks: a 1-D-CNN spectral mapping network and a 3-D-CNN spectral mapping network, trained class-specifically on clustered spectral samples. A spectral distance-based joint weighting mechanism integrates outputs from both networks to synthesize HS imagery spatially consistent with the input MS data. Experiments on four benchmark datasets demonstrate that JF-CNNSSR outperforms existing spectral super-resolution methods, achieving significant improvements across key quantitative metrics including peak signal-to-noise ratio, spectral angle mapper, structural similarity index measure, ERGAS, and MSE. Practical validation through grassland coverage estimation in China’s Three-River Source Region further confirms the superiority of reconstructed HS imagery over original MS data in ecological application accuracy.https://ieeexplore.ieee.org/document/11045064/Deep learninghyperspectral imaging (HSI)image fusionmultispectral imaging (MSI)spectral super-resolution (SSR) |
| spellingShingle | Wang Benlin Yu Qinglin Wang Zuo Li Weitao Wang Yong Liu Huan Gu Shuangxi Zhang Lingling Lv Dong Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning hyperspectral imaging (HSI) image fusion multispectral imaging (MSI) spectral super-resolution (SSR) |
| title | Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature |
| title_full | Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature |
| title_fullStr | Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature |
| title_full_unstemmed | Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature |
| title_short | Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature |
| title_sort | spectral super resolution reconstruction of multispectral remote sensing images via clustering based spectral feature |
| topic | Deep learning hyperspectral imaging (HSI) image fusion multispectral imaging (MSI) spectral super-resolution (SSR) |
| url | https://ieeexplore.ieee.org/document/11045064/ |
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