Sketched sparse subspace clustering for large-scale hyperspectral images
- Author
- Shaoguang Huang (UGent) , Hongyan Zhang and Aleksandra Pizurica (UGent)
- Organization
- Abstract
- Sparse subspace clustering (SSC) has achieved the state-of-the-art performance in clustering of hyperspectral images. However, the computational complexity of SSC-based methods is prohibitive for large-scale problems. We propose a large-scale SSC-based method, which processes efficiently large-scale HSIs without sacrificing the clustering accuracy. The proposed approach incorporates sketching of the self-representation dictionary reducing thereby largely the number of optimization variables. In addition, we employ a total variation (TV) regularization of the sparse matrix, resulting in a robust sparse representation. We derive a solver based on the alternating direction method of multipliers (ADMM) for the resulting optimization problem. Experimental results on real data show improvements over the traditional SSC-based methods in terms of accuracy and running time.
- Keywords
- Sparse subspace clustering, sketching, hyperspectral image, large-scale data
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8663403
- MLA
- Huang, Shaoguang, et al. “Sketched Sparse Subspace Clustering for Large-Scale Hyperspectral Images.” 2020 IEEE International Conference on Image Processing (ICIP), IEEE, 2020, pp. 1766–70, doi:10.1109/ICIP40778.2020.9191074.
- APA
- Huang, S., Zhang, H., & Pizurica, A. (2020). Sketched sparse subspace clustering for large-scale hyperspectral images. 2020 IEEE International Conference on Image Processing (ICIP), 1766–1770. https://doi.org/10.1109/ICIP40778.2020.9191074
- Chicago author-date
- Huang, Shaoguang, Hongyan Zhang, and Aleksandra Pizurica. 2020. “Sketched Sparse Subspace Clustering for Large-Scale Hyperspectral Images.” In 2020 IEEE International Conference on Image Processing (ICIP), 1766–70. IEEE. https://doi.org/10.1109/ICIP40778.2020.9191074.
- Chicago author-date (all authors)
- Huang, Shaoguang, Hongyan Zhang, and Aleksandra Pizurica. 2020. “Sketched Sparse Subspace Clustering for Large-Scale Hyperspectral Images.” In 2020 IEEE International Conference on Image Processing (ICIP), 1766–1770. IEEE. doi:10.1109/ICIP40778.2020.9191074.
- Vancouver
- 1.Huang S, Zhang H, Pizurica A. Sketched sparse subspace clustering for large-scale hyperspectral images. In: 2020 IEEE International Conference on Image Processing (ICIP). IEEE; 2020. p. 1766–70.
- IEEE
- [1]S. Huang, H. Zhang, and A. Pizurica, “Sketched sparse subspace clustering for large-scale hyperspectral images,” in 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, pp. 1766–1770.
@inproceedings{8663403,
abstract = {{Sparse subspace clustering (SSC) has achieved the state-of-the-art performance in clustering of hyperspectral images. However, the computational complexity of SSC-based methods is prohibitive for large-scale problems. We propose a large-scale SSC-based method, which processes efficiently large-scale HSIs without sacrificing the clustering accuracy. The proposed approach incorporates sketching of the self-representation dictionary reducing thereby largely the number of optimization variables. In addition, we employ a total variation (TV) regularization of the sparse matrix, resulting in a robust sparse representation. We derive a solver based on the alternating direction method of multipliers (ADMM) for the resulting optimization problem. Experimental results on real data show improvements over the traditional SSC-based methods in terms of accuracy and running time.}},
author = {{Huang, Shaoguang and Zhang, Hongyan and Pizurica, Aleksandra}},
booktitle = {{2020 IEEE International Conference on Image Processing (ICIP)}},
isbn = {{9781728163956}},
issn = {{1522-4880}},
keywords = {{Sparse subspace clustering,sketching,hyperspectral image,large-scale data}},
language = {{eng}},
location = {{Abu Dhabi, United Arab Emirates}},
pages = {{1766--1770}},
publisher = {{IEEE}},
title = {{Sketched sparse subspace clustering for large-scale hyperspectral images}},
url = {{http://doi.org/10.1109/ICIP40778.2020.9191074}},
year = {{2020}},
}
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