Multi-Scale Dense Graph Attention Network for Hyperspectral Classification
In recent years, numerous deep learning-based methods have gained increasing attention in hyperspectral classification, particularly the Graph Neural Network, which exhibits superior capabilities in structural description. However, a single graph structure is not suitable for hyperspectral feature r...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2024.2333424 |
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| _version_ | 1846095213851836416 |
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| author | Chen Wang Lu Li ZhongQi Wang JingYao Ma YunLong Kong YanFeng Wang JianRui Chang ZiMeng Zhang XinYu Lin |
| author_facet | Chen Wang Lu Li ZhongQi Wang JingYao Ma YunLong Kong YanFeng Wang JianRui Chang ZiMeng Zhang XinYu Lin |
| author_sort | Chen Wang |
| collection | DOAJ |
| description | In recent years, numerous deep learning-based methods have gained increasing attention in hyperspectral classification, particularly the Graph Neural Network, which exhibits superior capabilities in structural description. However, a single graph structure is not suitable for hyperspectral feature representation. Therefore, we propose a novel Multiple-Scale graph network structure, known as the Multi-Scale Dense Graph Attention network for hyperspectral classification. Firstly, semi-supervised local Fisher discriminant analysis and superpixel segmentation were employed for dimensionality reduction and multi-scale graph construction, respectively. Secondly, Spectral-Spatial convolution is applied to extract shallow features from the image. Subsequently, an improved graph self-attention network is sequentially applied to each scale graph, and the different scale graphs are densely connected through spatial feature alignment modules, designed using twice matrix multiplication. Finally, the combined pixel-level feature map from multiple graph spaces is derived, and Spectral-Spatial convolution is employed to fuse the abundant feature maps for hyperspectral classification. Experimental results on various hyperspectral datasets demonstrate the superiority of our MSDesGATnet over many state-of-the-art methods. The code is available at https://github.com/l7170/MSDesGAT.git. |
| format | Article |
| id | doaj-art-f74960e0bb0548b1bc674aa074652f06 |
| institution | Kabale University |
| issn | 1712-7971 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj-art-f74960e0bb0548b1bc674aa074652f062025-01-02T11:34:20ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712024-12-0150110.1080/07038992.2024.23334242333424Multi-Scale Dense Graph Attention Network for Hyperspectral ClassificationChen Wang0Lu Li1ZhongQi Wang2JingYao Ma3YunLong Kong4YanFeng Wang5JianRui Chang6ZiMeng Zhang7XinYu Lin8Department of Artificial Intelligence, School of Automation, Beijing Information Science and Technology UniversityDepartment of Artificial Intelligence, School of Automation, Beijing Information Science and Technology UniversityDepartment of Artificial Intelligence, School of Automation, Beijing Information Science and Technology UniversityDepartment of Artificial Intelligence, School of Automation, Beijing Information Science and Technology UniversityAerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesDepartment of Artificial Intelligence, School of Automation, Beijing Information Science and Technology UniversityDepartment of Artificial Intelligence, School of Automation, Beijing Information Science and Technology UniversityDepartment of Artificial Intelligence, School of Automation, Beijing Information Science and Technology UniversityIn recent years, numerous deep learning-based methods have gained increasing attention in hyperspectral classification, particularly the Graph Neural Network, which exhibits superior capabilities in structural description. However, a single graph structure is not suitable for hyperspectral feature representation. Therefore, we propose a novel Multiple-Scale graph network structure, known as the Multi-Scale Dense Graph Attention network for hyperspectral classification. Firstly, semi-supervised local Fisher discriminant analysis and superpixel segmentation were employed for dimensionality reduction and multi-scale graph construction, respectively. Secondly, Spectral-Spatial convolution is applied to extract shallow features from the image. Subsequently, an improved graph self-attention network is sequentially applied to each scale graph, and the different scale graphs are densely connected through spatial feature alignment modules, designed using twice matrix multiplication. Finally, the combined pixel-level feature map from multiple graph spaces is derived, and Spectral-Spatial convolution is employed to fuse the abundant feature maps for hyperspectral classification. Experimental results on various hyperspectral datasets demonstrate the superiority of our MSDesGATnet over many state-of-the-art methods. The code is available at https://github.com/l7170/MSDesGAT.git.http://dx.doi.org/10.1080/07038992.2024.2333424 |
| spellingShingle | Chen Wang Lu Li ZhongQi Wang JingYao Ma YunLong Kong YanFeng Wang JianRui Chang ZiMeng Zhang XinYu Lin Multi-Scale Dense Graph Attention Network for Hyperspectral Classification Canadian Journal of Remote Sensing |
| title | Multi-Scale Dense Graph Attention Network for Hyperspectral Classification |
| title_full | Multi-Scale Dense Graph Attention Network for Hyperspectral Classification |
| title_fullStr | Multi-Scale Dense Graph Attention Network for Hyperspectral Classification |
| title_full_unstemmed | Multi-Scale Dense Graph Attention Network for Hyperspectral Classification |
| title_short | Multi-Scale Dense Graph Attention Network for Hyperspectral Classification |
| title_sort | multi scale dense graph attention network for hyperspectral classification |
| url | http://dx.doi.org/10.1080/07038992.2024.2333424 |
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