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Exploiting the low-rank property of hyperpsectral imagery: a technical overview

Hongyan Zhang (UGent) , Wei He, Wenzhi Liao (UGent) , Renbo Luo (UGent) , Liangpei Zhang and Aleksandra Pizurica (UGent)
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Abstract
Hyperspectral images (HSIs) often suffer from various annoying degradations, which poses huge challenges for the practical applications. Fortunately, clean HSI is intrinsically low-rank, which opens up a broad category of HSI processing and analysis methods with high robustness against the complicated mixture of various noises and outliers. Based on the low rank property of HSI, this paper provides a comprehensive review on restoration, multi-angle registration and unmixing methods for HSIs developed very recently, and insights for further investigations.
Keywords
restoration, registration, Low-rank, hyperpsectral image, unminxing

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MLA
Zhang, Hongyan, et al. “Exploiting the Low-Rank Property of Hyperpsectral Imagery: A Technical Overview.” Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, edited by Jocelyn Chanussot, 2016, pp. 1–4.
APA
Zhang, H., He, W., Liao, W., Luo, R., Zhang, L., & Pizurica, A. (2016). Exploiting the low-rank property of hyperpsectral imagery: a technical overview. In J. Chanussot (Ed.), Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote sensing (pp. 1–4). Los Angeles, California, USA.
Chicago author-date
Zhang, Hongyan, Wei He, Wenzhi Liao, Renbo Luo, Liangpei Zhang, and Aleksandra Pizurica. 2016. “Exploiting the Low-Rank Property of Hyperpsectral Imagery: A Technical Overview.” In Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, edited by Jocelyn Chanussot, 1–4.
Chicago author-date (all authors)
Zhang, Hongyan, Wei He, Wenzhi Liao, Renbo Luo, Liangpei Zhang, and Aleksandra Pizurica. 2016. “Exploiting the Low-Rank Property of Hyperpsectral Imagery: A Technical Overview.” In Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, ed by. Jocelyn Chanussot, 1–4.
Vancouver
1.
Zhang H, He W, Liao W, Luo R, Zhang L, Pizurica A. Exploiting the low-rank property of hyperpsectral imagery: a technical overview. In: Chanussot J, editor. Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote sensing. 2016. p. 1–4.
IEEE
[1]
H. Zhang, W. He, W. Liao, R. Luo, L. Zhang, and A. Pizurica, “Exploiting the low-rank property of hyperpsectral imagery: a technical overview,” in Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote sensing, Los Angeles, California, USA, 2016, pp. 1–4.
@inproceedings{7221264,
  abstract     = {{Hyperspectral images (HSIs) often suffer from various annoying degradations, which poses huge challenges for the practical applications. Fortunately, clean HSI is intrinsically low-rank, which opens up a broad category of HSI processing and analysis methods with high robustness against the complicated mixture of various noises and outliers. Based on the low rank property of HSI, this paper provides a comprehensive review on restoration, multi-angle registration and unmixing methods for HSIs developed very recently, and insights for further investigations.}},
  author       = {{Zhang, Hongyan and He, Wei and Liao, Wenzhi and Luo, Renbo and Zhang, Liangpei and Pizurica, Aleksandra}},
  booktitle    = {{Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote sensing}},
  editor       = {{Chanussot, Jocelyn}},
  keywords     = {{restoration,registration,Low-rank,hyperpsectral image,unminxing}},
  language     = {{eng}},
  location     = {{Los Angeles, California, USA}},
  pages        = {{1--4}},
  title        = {{Exploiting the low-rank property of hyperpsectral imagery: a technical overview}},
  year         = {{2016}},
}