Transformer-Based Multi-Scale Feature Remote Sensing Image Classification Model
To address the challenge of poor classification accuracy due to complex backgrounds, large intra-scale variations, and high inter-scale similarity in remote sensing scene classification (RSSC), we propose a new remote sensing scene classification model called multi-scale dual-branch classification n...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10807217/ |
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| author | Ting Sun Jun Li Xiangrui Zhou Zan Chen |
| author_facet | Ting Sun Jun Li Xiangrui Zhou Zan Chen |
| author_sort | Ting Sun |
| collection | DOAJ |
| description | To address the challenge of poor classification accuracy due to complex backgrounds, large intra-scale variations, and high inter-scale similarity in remote sensing scene classification (RSSC), we propose a new remote sensing scene classification model called multi-scale dual-branch classification network (MDBC-Net). The model is composed of a Trans-branch and CNN-branch in parallel, which can fully utilize the local attention of the CNN-branch structure and the global attention mechanism of the Trans-branch structure, thereby improving the model’s ability to focus on features of different scales. Due to the complexity of backgrounds in RSSC, we require features at different scales to obtain richer scene information. Thus we design a down-sampling module in the model to obtain multi-scale features. Finally, we adopt the polynomial form of cross entropy for the trained loss function to improve the generalization performance and robustness of the model. Experiments have shown that the model achieves advanced performance on three datasets: NWPU-RESISC45, AID, and UC Served. |
| format | Article |
| id | doaj-art-42b37eb41f324538bd0d5a645c97e24c |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-42b37eb41f324538bd0d5a645c97e24c2025-08-20T02:45:31ZengIEEEIEEE Access2169-35362025-01-0113340953410410.1109/ACCESS.2024.352025310807217Transformer-Based Multi-Scale Feature Remote Sensing Image Classification ModelTing Sun0https://orcid.org/0009-0009-2956-1577Jun Li1https://orcid.org/0009-0004-1683-5130Xiangrui Zhou2Zan Chen3https://orcid.org/0000-0003-4252-4761School of Culture and Tourism, Zhejiang International Studies University, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaTo address the challenge of poor classification accuracy due to complex backgrounds, large intra-scale variations, and high inter-scale similarity in remote sensing scene classification (RSSC), we propose a new remote sensing scene classification model called multi-scale dual-branch classification network (MDBC-Net). The model is composed of a Trans-branch and CNN-branch in parallel, which can fully utilize the local attention of the CNN-branch structure and the global attention mechanism of the Trans-branch structure, thereby improving the model’s ability to focus on features of different scales. Due to the complexity of backgrounds in RSSC, we require features at different scales to obtain richer scene information. Thus we design a down-sampling module in the model to obtain multi-scale features. Finally, we adopt the polynomial form of cross entropy for the trained loss function to improve the generalization performance and robustness of the model. Experiments have shown that the model achieves advanced performance on three datasets: NWPU-RESISC45, AID, and UC Served.https://ieeexplore.ieee.org/document/10807217/Remote sensingscene classificationmulti-scale featureslocal attentionglobal attentionpolynomial form |
| spellingShingle | Ting Sun Jun Li Xiangrui Zhou Zan Chen Transformer-Based Multi-Scale Feature Remote Sensing Image Classification Model IEEE Access Remote sensing scene classification multi-scale features local attention global attention polynomial form |
| title | Transformer-Based Multi-Scale Feature Remote Sensing Image Classification Model |
| title_full | Transformer-Based Multi-Scale Feature Remote Sensing Image Classification Model |
| title_fullStr | Transformer-Based Multi-Scale Feature Remote Sensing Image Classification Model |
| title_full_unstemmed | Transformer-Based Multi-Scale Feature Remote Sensing Image Classification Model |
| title_short | Transformer-Based Multi-Scale Feature Remote Sensing Image Classification Model |
| title_sort | transformer based multi scale feature remote sensing image classification model |
| topic | Remote sensing scene classification multi-scale features local attention global attention polynomial form |
| url | https://ieeexplore.ieee.org/document/10807217/ |
| work_keys_str_mv | AT tingsun transformerbasedmultiscalefeatureremotesensingimageclassificationmodel AT junli transformerbasedmultiscalefeatureremotesensingimageclassificationmodel AT xiangruizhou transformerbasedmultiscalefeatureremotesensingimageclassificationmodel AT zanchen transformerbasedmultiscalefeatureremotesensingimageclassificationmodel |