Multisource remote sensing data classification using fractional Fourier transformer
- Author
- Xudong Zhao, Mengmeng Zhang, Ran Tao, Wei Li, Wenzhi Liao (UGent) and Wilfried Philips (UGent)
- Organization
- Project
- Abstract
- Focusing on joint classification of Hyperspectral image (HSI) and Light detection and ranging (LiDAR) data, a fractional Fourier image transformer (FrIT) is proposed as a backbone network in this paper. In the proposed FrIT, HSI and LiDAR data are firstly fused at pixel-level. Both multi-source and HSI feature extractors are utilized to capture local contexts. Then, a plug-and-play image transformer FrIT is explored for global contexts and sequential feature extraction. Unlike the attention-based representations in classic visual image transformer (VIT), FrIT is capable of speeding up the transformer architectures massively. To reduce the information loss from shallow to deep layers, FrIT is devised to connect contextual features in multiple fractional domains. At last, to evaluate the performance of FrIT, a new HSI and LiDAR benchmark is provided for extensive experiments, on which the proposed FrIT gains an improvement of 3% over state-of-the-art methods.
- Keywords
- Fractional Fourier image transformer (FrIT), hyperspectral image (HSI), light detection and ranging (LiDAR), multisource remote sensing, LIDAR
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01GMB95VA80KJ55WDGGNVFG732
- MLA
- Zhao, Xudong, et al. “Multisource Remote Sensing Data Classification Using Fractional Fourier Transformer.” IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2022, pp. 823–26, doi:10.1109/igarss46834.2022.9884573.
- APA
- Zhao, X., Zhang, M., Tao, R., Li, W., Liao, W., & Philips, W. (2022). Multisource remote sensing data classification using fractional Fourier transformer. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 823–826. https://doi.org/10.1109/igarss46834.2022.9884573
- Chicago author-date
- Zhao, Xudong, Mengmeng Zhang, Ran Tao, Wei Li, Wenzhi Liao, and Wilfried Philips. 2022. “Multisource Remote Sensing Data Classification Using Fractional Fourier Transformer.” In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 823–26. IEEE. https://doi.org/10.1109/igarss46834.2022.9884573.
- Chicago author-date (all authors)
- Zhao, Xudong, Mengmeng Zhang, Ran Tao, Wei Li, Wenzhi Liao, and Wilfried Philips. 2022. “Multisource Remote Sensing Data Classification Using Fractional Fourier Transformer.” In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 823–826. IEEE. doi:10.1109/igarss46834.2022.9884573.
- Vancouver
- 1.Zhao X, Zhang M, Tao R, Li W, Liao W, Philips W. Multisource remote sensing data classification using fractional Fourier transformer. In: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE; 2022. p. 823–6.
- IEEE
- [1]X. Zhao, M. Zhang, R. Tao, W. Li, W. Liao, and W. Philips, “Multisource remote sensing data classification using fractional Fourier transformer,” in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 823–826.
@inproceedings{01GMB95VA80KJ55WDGGNVFG732,
abstract = {{Focusing on joint classification of Hyperspectral image (HSI) and Light detection and ranging (LiDAR) data, a fractional Fourier image transformer (FrIT) is proposed as a backbone network in this paper. In the proposed FrIT, HSI and LiDAR data are firstly fused at pixel-level. Both multi-source and HSI feature extractors are utilized to capture local contexts. Then, a plug-and-play image transformer FrIT is explored for global contexts and sequential feature extraction. Unlike the attention-based representations in classic visual image transformer (VIT), FrIT is capable of speeding up the transformer architectures massively. To reduce the information loss from shallow to deep layers, FrIT is devised to connect contextual features in multiple fractional domains. At last, to evaluate the performance of FrIT, a new HSI and LiDAR benchmark is provided for extensive experiments, on which the proposed FrIT gains an improvement of 3% over state-of-the-art methods.}},
author = {{Zhao, Xudong and Zhang, Mengmeng and Tao, Ran and Li, Wei and Liao, Wenzhi and Philips, Wilfried}},
booktitle = {{IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium}},
isbn = {{9781665427920}},
issn = {{2153-6996}},
keywords = {{Fractional Fourier image transformer (FrIT),hyperspectral image (HSI),light detection and ranging (LiDAR),multisource remote sensing,LIDAR}},
language = {{eng}},
location = {{Kuala Lumpur, Malaysia}},
pages = {{823--826}},
publisher = {{IEEE}},
title = {{Multisource remote sensing data classification using fractional Fourier transformer}},
url = {{http://doi.org/10.1109/igarss46834.2022.9884573}},
year = {{2022}},
}
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