Global–Local Multigranularity Transformer for Hyperspectral Image Classification
Hyperspectral image (HSI) classification is a challenging task in remote sensing applications, aiming to determine the category of each pixel by utilizing rich spectral and spatial information in HSI. Convolutional neural networks (CNNs) have been effective in processing HSI data by extracting local...
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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/10746388/ |
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| author | Zhe Meng Qian Yan Feng Zhao Gaige Chen Wenqiang Hua Miaomiao Liang |
| author_facet | Zhe Meng Qian Yan Feng Zhao Gaige Chen Wenqiang Hua Miaomiao Liang |
| author_sort | Zhe Meng |
| collection | DOAJ |
| description | Hyperspectral image (HSI) classification is a challenging task in remote sensing applications, aiming to determine the category of each pixel by utilizing rich spectral and spatial information in HSI. Convolutional neural networks (CNNs) have been effective in processing HSI data by extracting local features, but they are deficient in capturing global contextual information. Recently, transformer has become proficient in attending to global information due to their self-attention mechanisms, yet they may fall short in capturing multiscale features of HSI. To address these limitations, a global–local multigranularity transformer (GLMGT) network is proposed for HSI classification. The GLMGT combines CNN with the transformer to comprehensively capture multigranularity spectral and spatial features across global and local scales. Specifically, we introduce a multigranularity spatial feature extraction block to extensively extract spatial information at different granularities, including multiscale local spatial features and global spatial features. In addition, we introduce a multigranularity spectral feature extraction block to fully leverage spectral information across different granularities. The validity of the proposed method is demonstrated through experimental validation using seven publicly available datasets, which include two Chinese satellite hyperspectral datasets (ZY1-02D Huanghekou and GF-5 Yancheng) and one UAV-based hyperspectral dataset. |
| format | Article |
| id | doaj-art-e87fcdd975e74ecbbcd27b68cfc077c1 |
| 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-e87fcdd975e74ecbbcd27b68cfc077c12025-08-20T01:53:36ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011811213110.1109/JSTARS.2024.349129410746388Global–Local Multigranularity Transformer for Hyperspectral Image ClassificationZhe Meng0https://orcid.org/0000-0002-2364-2749Qian Yan1https://orcid.org/0009-0006-3425-211XFeng Zhao2https://orcid.org/0000-0002-0323-9573Gaige Chen3https://orcid.org/0000-0002-3045-6514Wenqiang Hua4https://orcid.org/0000-0003-2611-6194Miaomiao Liang5https://orcid.org/0000-0002-4289-7114School of Communications and Information Engineering and School of Artificial Intelligence, Xi'an University of Posts and Telecommunications, Xi'an, ChinaSchool of Communications and Information Engineering and School of Artificial Intelligence, Xi'an University of Posts and Telecommunications, Xi'an, ChinaSchool of Communications and Information Engineering and School of Artificial Intelligence, Xi'an University of Posts and Telecommunications, Xi'an, ChinaSchool of Communications and Information Engineering and School of Artificial Intelligence, Xi'an University of Posts and Telecommunications, Xi'an, ChinaSchool of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, ChinaJiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control, School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, ChinaHyperspectral image (HSI) classification is a challenging task in remote sensing applications, aiming to determine the category of each pixel by utilizing rich spectral and spatial information in HSI. Convolutional neural networks (CNNs) have been effective in processing HSI data by extracting local features, but they are deficient in capturing global contextual information. Recently, transformer has become proficient in attending to global information due to their self-attention mechanisms, yet they may fall short in capturing multiscale features of HSI. To address these limitations, a global–local multigranularity transformer (GLMGT) network is proposed for HSI classification. The GLMGT combines CNN with the transformer to comprehensively capture multigranularity spectral and spatial features across global and local scales. Specifically, we introduce a multigranularity spatial feature extraction block to extensively extract spatial information at different granularities, including multiscale local spatial features and global spatial features. In addition, we introduce a multigranularity spectral feature extraction block to fully leverage spectral information across different granularities. The validity of the proposed method is demonstrated through experimental validation using seven publicly available datasets, which include two Chinese satellite hyperspectral datasets (ZY1-02D Huanghekou and GF-5 Yancheng) and one UAV-based hyperspectral dataset.https://ieeexplore.ieee.org/document/10746388/CNNmultigranularitytransformerhyperspectral image (HSI) classification |
| spellingShingle | Zhe Meng Qian Yan Feng Zhao Gaige Chen Wenqiang Hua Miaomiao Liang Global–Local Multigranularity Transformer for Hyperspectral Image Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing CNN multigranularity transformer hyperspectral image (HSI) classification |
| title | Global–Local Multigranularity Transformer for Hyperspectral Image Classification |
| title_full | Global–Local Multigranularity Transformer for Hyperspectral Image Classification |
| title_fullStr | Global–Local Multigranularity Transformer for Hyperspectral Image Classification |
| title_full_unstemmed | Global–Local Multigranularity Transformer for Hyperspectral Image Classification |
| title_short | Global–Local Multigranularity Transformer for Hyperspectral Image Classification |
| title_sort | global x2013 local multigranularity transformer for hyperspectral image classification |
| topic | CNN multigranularity transformer hyperspectral image (HSI) classification |
| url | https://ieeexplore.ieee.org/document/10746388/ |
| work_keys_str_mv | AT zhemeng globalx2013localmultigranularitytransformerforhyperspectralimageclassification AT qianyan globalx2013localmultigranularitytransformerforhyperspectralimageclassification AT fengzhao globalx2013localmultigranularitytransformerforhyperspectralimageclassification AT gaigechen globalx2013localmultigranularitytransformerforhyperspectralimageclassification AT wenqianghua globalx2013localmultigranularitytransformerforhyperspectralimageclassification AT miaomiaoliang globalx2013localmultigranularitytransformerforhyperspectralimageclassification |