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

Hongyan Zhang, Wei He, Wenzhi Liao, Renbo Luo, Liangpei Zhang, Aleksandra Pizurica, Jocelyn Chanussot

Research output: Contribution to conferencePaper

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.

Workshop

Workshop2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Abbreviated titleWHISPERS 2016
CountryUnited States
CityLos Angeles
Period21/08/1624/08/16

Fingerprint

imagery
Image analysis
Restoration
Image processing
Degradation
image analysis
restoration
image processing
degradation

Keywords

  • restoration
  • registration
  • Low-rank
  • hyperpsectral image
  • unminxing

Cite this

Zhang, H., He, W., Liao, W., Luo, R., Zhang, L., Pizurica, A., & Chanussot, J. (2017). Exploiting the low-rank property of hyperpsectral imagery: a technical overview. 1-4. Paper presented at 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, United States. https://doi.org/10.1109/WHISPERS.2016.8071731
Zhang, Hongyan ; He, Wei ; Liao, Wenzhi ; Luo, Renbo ; Zhang, Liangpei ; Pizurica, Aleksandra ; Chanussot, Jocelyn. / Exploiting the low-rank property of hyperpsectral imagery : a technical overview. Paper presented at 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, United States.4 p.
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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",
author = "Hongyan Zhang and Wei He and Wenzhi Liao and Renbo Luo and Liangpei Zhang and Aleksandra Pizurica and Jocelyn Chanussot",
year = "2017",
month = "10",
day = "19",
doi = "10.1109/WHISPERS.2016.8071731",
language = "English",
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note = "2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), WHISPERS 2016 ; Conference date: 21-08-2016 Through 24-08-2016",

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Zhang, H, He, W, Liao, W, Luo, R, Zhang, L, Pizurica, A & Chanussot, J 2017, 'Exploiting the low-rank property of hyperpsectral imagery: a technical overview' Paper presented at 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, United States, 21/08/16 - 24/08/16, pp. 1-4. https://doi.org/10.1109/WHISPERS.2016.8071731

Exploiting the low-rank property of hyperpsectral imagery : a technical overview. / Zhang, Hongyan; He, Wei; Liao, Wenzhi; Luo, Renbo; Zhang, Liangpei; Pizurica, Aleksandra; Chanussot, Jocelyn.

2017. 1-4 Paper presented at 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Exploiting the low-rank property of hyperpsectral imagery

T2 - a technical overview

AU - Zhang, Hongyan

AU - He, Wei

AU - Liao, Wenzhi

AU - Luo, Renbo

AU - Zhang, Liangpei

AU - Pizurica, Aleksandra

AU - Chanussot, Jocelyn

PY - 2017/10/19

Y1 - 2017/10/19

N2 - 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.

AB - 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.

KW - restoration

KW - registration

KW - Low-rank

KW - hyperpsectral image

KW - unminxing

UR - http://hdl.handle.net/1854/LU-7221264

U2 - 10.1109/WHISPERS.2016.8071731

DO - 10.1109/WHISPERS.2016.8071731

M3 - Paper

SP - 1

EP - 4

ER -

Zhang H, He W, Liao W, Luo R, Zhang L, Pizurica A et al. Exploiting the low-rank property of hyperpsectral imagery: a technical overview. 2017. Paper presented at 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, United States. https://doi.org/10.1109/WHISPERS.2016.8071731