Landmark-based large-scale sparse subspace clustering method for 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 the clustering of hyperspectral images (HSIs). However, the high computational complexity and sensitivity to noise limit its clustering performance. In this paper, we propose a scalable SSC method for the large-scale HSIs, which significantly accelerates the clustering speed of SSC without sacrificing clustering accuracy. A small landmark dictionary is first generated by applying k-means to the original data, which results in the significant reduction of the number of optimization variables in terms of sparse matrix. In addition, we incorporate spatial regularization based on total variation (TV) and improve this way strongly robustness to noise. A landmark-based spectral clustering method is applied to the obtained sparse matrix, which further improves the clustering speed. Experimental results on two real HSIs demonstrate the effectiveness of the proposed method and the superior performance compared to both traditional SSC-based methods and the related large-scale clustering methods.
- Keywords
- Sparse subspace clustering, landmark, 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-8623398
- MLA
- Huang, Shaoguang, et al. “Landmark-Based Large-Scale Sparse Subspace Clustering Method for Hyperspectral Images.” 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), IEEE, 2019, pp. 799–802.
- APA
- Huang, S., Zhang, H., & Pizurica, A. (2019). Landmark-based large-scale sparse subspace clustering method for hyperspectral images. In 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) (pp. 799–802). NEW YORK: IEEE.
- Chicago author-date
- Huang, Shaoguang, Hongyan Zhang, and Aleksandra Pizurica. 2019. “Landmark-Based Large-Scale Sparse Subspace Clustering Method for Hyperspectral Images.” In 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 799–802. NEW YORK: IEEE.
- Chicago author-date (all authors)
- Huang, Shaoguang, Hongyan Zhang, and Aleksandra Pizurica. 2019. “Landmark-Based Large-Scale Sparse Subspace Clustering Method for Hyperspectral Images.” In 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 799–802. NEW YORK: IEEE.
- Vancouver
- 1.Huang S, Zhang H, Pizurica A. Landmark-based large-scale sparse subspace clustering method for hyperspectral images. In: 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS). NEW YORK: IEEE; 2019. p. 799–802.
- IEEE
- [1]S. Huang, H. Zhang, and A. Pizurica, “Landmark-based large-scale sparse subspace clustering method for hyperspectral images,” in 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), Yokohama, Japan, 2019, pp. 799–802.
@inproceedings{8623398,
abstract = {{Sparse subspace clustering (SSC) has achieved the state-of-the-art performance in the clustering of hyperspectral images (HSIs). However, the high computational complexity and sensitivity to noise limit its clustering performance. In this paper, we propose a scalable SSC method for the large-scale HSIs, which significantly accelerates the clustering speed of SSC without sacrificing clustering accuracy. A small landmark dictionary is first generated by applying k-means to the original data, which results in the significant reduction of the number of optimization variables in terms of sparse matrix. In addition, we incorporate spatial regularization based on total variation (TV) and improve this way strongly robustness to noise. A landmark-based spectral clustering method is applied to the obtained sparse matrix, which further improves the clustering speed. Experimental results on two real HSIs demonstrate the effectiveness of the proposed method and the superior performance compared to both traditional SSC-based methods and the related large-scale clustering methods.}},
author = {{Huang, Shaoguang and Zhang, Hongyan and Pizurica, Aleksandra}},
booktitle = {{2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)}},
isbn = {{9781538691540}},
issn = {{2153-6996}},
keywords = {{Sparse subspace clustering,landmark,hyperspectral image,large-scale data}},
language = {{eng}},
location = {{Yokohama, Japan}},
pages = {{799--802}},
publisher = {{IEEE}},
title = {{Landmark-based large-scale sparse subspace clustering method for hyperspectral images}},
url = {{http://dx.doi.org/10.1109/IGARSS.2019.8898869}},
year = {{2019}},
}
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