Exploiting the low-rank property of hyperpsectral imagery: a technical overview
- Author
- Hongyan Zhang (UGent) , Wei He, Wenzhi Liao (UGent) , Renbo Luo (UGent) , Liangpei Zhang and Aleksandra Pizurica (UGent)
- Organization
- 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
Downloads
-
(...).pdf
- full text
- |
- UGent only
- |
- |
- 477.87 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-7221264
- 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).
- 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}},
}