Multisource cross-scene classification using fractional fusion and spatial-spectral domain adaptation
- Author
- Xudong Zhao, Mengmeng Zhang, Ran Tao, Wei Li, Wenzhi Liao (UGent) and Wilfried Philips (UGent)
- Organization
- Project
- Abstract
- To solve the limitation of labeled samples in hyperspectral image (HSI) classification, cross-scene learning methods are developed recently. However, the disparity caused by environmental variation between HSI scenes is still a challenge. As a supplement, light detection and ranging (LiDAR) data provides elevation and spatial information regardless the variations. In this paper, we propose a multisource cross-scene classification method using fractional fusion and spatial-spectral domain adaptation to reduce disparity between scenes. The spatial information of HSI is preserved by fractional differential masks (FrDM) firstly. Then the LiDAR data is utilized for spectral alignment of HSI. The utilization of LiDAR data reduces the pixel-level disparity between scenes. At last, a spatial-spectral domain adaptation network is proposed for feature extraction and classification. Experimental results on HSI and LiDAR scenes show 5% improvements in overall accuracy compared with state-of-the-art methods.
- Keywords
- Fractional fusion (FrF), spatial-spectral domain adaptation (SSDA), hyperspectral image (HSI), light detection and ranging (LiDAR), cross-scene classification
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01GMB8XV6CT92JKRWJKZ104XSX
- MLA
- Zhao, Xudong, et al. “Multisource Cross-Scene Classification Using Fractional Fusion and Spatial-Spectral Domain Adaptation.” IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2022, pp. 699–702, doi:10.1109/igarss46834.2022.9884659.
- APA
- Zhao, X., Zhang, M., Tao, R., Li, W., Liao, W., & Philips, W. (2022). Multisource cross-scene classification using fractional fusion and spatial-spectral domain adaptation. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 699–702. https://doi.org/10.1109/igarss46834.2022.9884659
- Chicago author-date
- Zhao, Xudong, Mengmeng Zhang, Ran Tao, Wei Li, Wenzhi Liao, and Wilfried Philips. 2022. “Multisource Cross-Scene Classification Using Fractional Fusion and Spatial-Spectral Domain Adaptation.” In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 699–702. IEEE. https://doi.org/10.1109/igarss46834.2022.9884659.
- Chicago author-date (all authors)
- Zhao, Xudong, Mengmeng Zhang, Ran Tao, Wei Li, Wenzhi Liao, and Wilfried Philips. 2022. “Multisource Cross-Scene Classification Using Fractional Fusion and Spatial-Spectral Domain Adaptation.” In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 699–702. IEEE. doi:10.1109/igarss46834.2022.9884659.
- Vancouver
- 1.Zhao X, Zhang M, Tao R, Li W, Liao W, Philips W. Multisource cross-scene classification using fractional fusion and spatial-spectral domain adaptation. In: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE; 2022. p. 699–702.
- IEEE
- [1]X. Zhao, M. Zhang, R. Tao, W. Li, W. Liao, and W. Philips, “Multisource cross-scene classification using fractional fusion and spatial-spectral domain adaptation,” in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 699–702.
@inproceedings{01GMB8XV6CT92JKRWJKZ104XSX,
abstract = {{To solve the limitation of labeled samples in hyperspectral image (HSI) classification, cross-scene learning methods are developed recently. However, the disparity caused by environmental variation between HSI scenes is still a challenge. As a supplement, light detection and ranging (LiDAR) data provides elevation and spatial information regardless the variations. In this paper, we propose a multisource cross-scene classification method using fractional fusion and spatial-spectral domain adaptation to reduce disparity between scenes. The spatial information of HSI is preserved by fractional differential masks (FrDM) firstly. Then the LiDAR data is utilized for spectral alignment of HSI. The utilization of LiDAR data reduces the pixel-level disparity between scenes. At last, a spatial-spectral domain adaptation network is proposed for feature extraction and classification. Experimental results on HSI and LiDAR scenes show 5% improvements in overall accuracy compared with 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 fusion (FrF),spatial-spectral domain adaptation (SSDA),hyperspectral image (HSI),light detection and ranging (LiDAR),cross-scene classification}},
language = {{eng}},
location = {{Kuala Lumpur, Malaysia}},
pages = {{699--702}},
publisher = {{IEEE}},
title = {{Multisource cross-scene classification using fractional fusion and spatial-spectral domain adaptation}},
url = {{http://doi.org/10.1109/igarss46834.2022.9884659}},
year = {{2022}},
}
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