PICT-Net: A Transformer-Based Network with Prior Information Correction for Hyperspectral Image Unmixing
Transformers have performed favorably in recent hyperspectral unmixing studies in which the self-attention mechanism possesses the ability to retain spectral information and spatial details. However, the lack of reliable prior information for correction guidance has resulted in an inadequate accurac...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/869 |
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| author | Yiliang Zeng Na Meng Jinlin Zou Wenbin Liu |
| author_facet | Yiliang Zeng Na Meng Jinlin Zou Wenbin Liu |
| author_sort | Yiliang Zeng |
| collection | DOAJ |
| description | Transformers have performed favorably in recent hyperspectral unmixing studies in which the self-attention mechanism possesses the ability to retain spectral information and spatial details. However, the lack of reliable prior information for correction guidance has resulted in an inadequate accuracy and robustness of the network. To benefit from the advantages of the Transformer architecture and to improve the interpretability and robustness of the network, a dual-branch network with prior information correction, incorporating a Transformer network (PICT-Net), is proposed. The upper branch utilizes pre-extracted endmembers to provide pure pixel prior information. The lower branch employs a Transformer structure for feature extraction and unmixing processing. A weight-sharing strategy is employed between the two branches to facilitate information sharing. The deep integration of prior knowledge into the Transformer architecture effectively reduces endmember variability in hyperspectral unmixing and enhances the model’s generalization capability and accuracy across diverse scenarios. Experimental results from experiments conducted on four real datasets demonstrate the effectiveness and superiority of the proposed model. |
| format | Article |
| id | doaj-art-e616be08141940e89d56f6f8227b173a |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-e616be08141940e89d56f6f8227b173a2025-08-20T02:06:13ZengMDPI AGRemote Sensing2072-42922025-02-0117586910.3390/rs17050869PICT-Net: A Transformer-Based Network with Prior Information Correction for Hyperspectral Image UnmixingYiliang Zeng0Na Meng1Jinlin Zou2Wenbin Liu3Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation, University of Science and Technology Beijing, Beijing 100083, ChinaEquipment Management and UAV Engineering School, Air Force Engineering University, Xi’an 710043, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation, University of Science and Technology Beijing, Beijing 100083, ChinaTransformers have performed favorably in recent hyperspectral unmixing studies in which the self-attention mechanism possesses the ability to retain spectral information and spatial details. However, the lack of reliable prior information for correction guidance has resulted in an inadequate accuracy and robustness of the network. To benefit from the advantages of the Transformer architecture and to improve the interpretability and robustness of the network, a dual-branch network with prior information correction, incorporating a Transformer network (PICT-Net), is proposed. The upper branch utilizes pre-extracted endmembers to provide pure pixel prior information. The lower branch employs a Transformer structure for feature extraction and unmixing processing. A weight-sharing strategy is employed between the two branches to facilitate information sharing. The deep integration of prior knowledge into the Transformer architecture effectively reduces endmember variability in hyperspectral unmixing and enhances the model’s generalization capability and accuracy across diverse scenarios. Experimental results from experiments conducted on four real datasets demonstrate the effectiveness and superiority of the proposed model.https://www.mdpi.com/2072-4292/17/5/869convolutional neural networkhyperspectral imageryhyperspectral unmixingspatial–spectraltransformerdual-branch network |
| spellingShingle | Yiliang Zeng Na Meng Jinlin Zou Wenbin Liu PICT-Net: A Transformer-Based Network with Prior Information Correction for Hyperspectral Image Unmixing Remote Sensing convolutional neural network hyperspectral imagery hyperspectral unmixing spatial–spectral transformer dual-branch network |
| title | PICT-Net: A Transformer-Based Network with Prior Information Correction for Hyperspectral Image Unmixing |
| title_full | PICT-Net: A Transformer-Based Network with Prior Information Correction for Hyperspectral Image Unmixing |
| title_fullStr | PICT-Net: A Transformer-Based Network with Prior Information Correction for Hyperspectral Image Unmixing |
| title_full_unstemmed | PICT-Net: A Transformer-Based Network with Prior Information Correction for Hyperspectral Image Unmixing |
| title_short | PICT-Net: A Transformer-Based Network with Prior Information Correction for Hyperspectral Image Unmixing |
| title_sort | pict net a transformer based network with prior information correction for hyperspectral image unmixing |
| topic | convolutional neural network hyperspectral imagery hyperspectral unmixing spatial–spectral transformer dual-branch network |
| url | https://www.mdpi.com/2072-4292/17/5/869 |
| work_keys_str_mv | AT yiliangzeng pictnetatransformerbasednetworkwithpriorinformationcorrectionforhyperspectralimageunmixing AT nameng pictnetatransformerbasednetworkwithpriorinformationcorrectionforhyperspectralimageunmixing AT jinlinzou pictnetatransformerbasednetworkwithpriorinformationcorrectionforhyperspectralimageunmixing AT wenbinliu pictnetatransformerbasednetworkwithpriorinformationcorrectionforhyperspectralimageunmixing |