GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image Classification
Convolutional neural networks and transformers have been extensively utilized in hyperspectral image classification due to their exceptional feature learning capabilities. However, many existing patch-based classification methods often neglect the fusion of multiscale and multiangle features and can...
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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/11117185/ |
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| _version_ | 1849228533630500864 |
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| author | Junding Sun Haoxiang Dong Yanlong Gao Xiaosheng Wu Jianlong Wang Yudong Zhang |
| author_facet | Junding Sun Haoxiang Dong Yanlong Gao Xiaosheng Wu Jianlong Wang Yudong Zhang |
| author_sort | Junding Sun |
| collection | DOAJ |
| description | Convolutional neural networks and transformers have been extensively utilized in hyperspectral image classification due to their exceptional feature learning capabilities. However, many existing patch-based classification methods often neglect the fusion of multiscale and multiangle features and cannot fully capture the relationships between the central pixel and its neighboring pixels, which is likely to compromise the classification performance. To address these challenges, this article proposes a grouped multiscale multiangle feature fusion network with center attention (GM2FFNet). The proposed network unfolds in three key stages. First, a grouped multiscale extraction module captures and fuses spectral features at different scales using various kernels. Second, a grouped multiangle convolution module extracts features from multiple directions, with an adaptive fusion module further integrating this information. Finally, a spatial-spectral attention transformer module captures the correlations between the central pixel and its surrounding pixels. Experimental results on the Indian Pines, Pavia University, and Hi-LongKou datasets show that GM2FFNet achieves overall accuracies of 98.17%, 98.45%, and 99.03%, respectively, using only 7%, 0.7%, and 0.2% of the labeled samples, with a reduced number of parameters. Notably, the model significantly outperforms existing methods in several challenging categories across all three datasets. These results highlight both the effectiveness and robustness of the proposed network. |
| format | Article |
| id | doaj-art-aff87b870f964fab9aeda8e8e487fda3 |
| 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-aff87b870f964fab9aeda8e8e487fda32025-08-22T23:08:05ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118203752038910.1109/JSTARS.2025.359637811117185GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image ClassificationJunding Sun0https://orcid.org/0000-0001-7349-0248Haoxiang Dong1Yanlong Gao2https://orcid.org/0000-0002-3696-6398Xiaosheng Wu3https://orcid.org/0000-0003-1688-9564Jianlong Wang4https://orcid.org/0000-0001-8117-0631Yudong Zhang5https://orcid.org/0000-0002-4870-1493School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, ChinaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester, U.K.Convolutional neural networks and transformers have been extensively utilized in hyperspectral image classification due to their exceptional feature learning capabilities. However, many existing patch-based classification methods often neglect the fusion of multiscale and multiangle features and cannot fully capture the relationships between the central pixel and its neighboring pixels, which is likely to compromise the classification performance. To address these challenges, this article proposes a grouped multiscale multiangle feature fusion network with center attention (GM2FFNet). The proposed network unfolds in three key stages. First, a grouped multiscale extraction module captures and fuses spectral features at different scales using various kernels. Second, a grouped multiangle convolution module extracts features from multiple directions, with an adaptive fusion module further integrating this information. Finally, a spatial-spectral attention transformer module captures the correlations between the central pixel and its surrounding pixels. Experimental results on the Indian Pines, Pavia University, and Hi-LongKou datasets show that GM2FFNet achieves overall accuracies of 98.17%, 98.45%, and 99.03%, respectively, using only 7%, 0.7%, and 0.2% of the labeled samples, with a reduced number of parameters. Notably, the model significantly outperforms existing methods in several challenging categories across all three datasets. These results highlight both the effectiveness and robustness of the proposed network.https://ieeexplore.ieee.org/document/11117185/Hyperspectral image (HSI) classificationfeature fusioncentral pixelconvolutional neural network (CNN)transformer |
| spellingShingle | Junding Sun Haoxiang Dong Yanlong Gao Xiaosheng Wu Jianlong Wang Yudong Zhang GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Hyperspectral image (HSI) classification feature fusion central pixel convolutional neural network (CNN) transformer |
| title | GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image Classification |
| title_full | GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image Classification |
| title_fullStr | GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image Classification |
| title_full_unstemmed | GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image Classification |
| title_short | GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image Classification |
| title_sort | gm2ffnet grouped multiscale multiangle feature fusion network with center attention for hyperspectral image classification |
| topic | Hyperspectral image (HSI) classification feature fusion central pixel convolutional neural network (CNN) transformer |
| url | https://ieeexplore.ieee.org/document/11117185/ |
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