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

doi:10.1109/WHISPERS.2016.8071731

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

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
Pages	1-4
Number of pages	4
DOIs	https://doi.org/10.1109/WHISPERS.2016.8071731
Publication status	Published - 19 Oct 2017
Event	2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Los Angeles, United States Duration: 21 Aug 2016 → 24 Aug 2016

Workshop

Workshop	2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Abbreviated title	WHISPERS 2016
Country/Territory	United States
City	Los Angeles
Period	21/08/16 → 24/08/16

Keywords

restoration
registration
Low-rank
hyperpsectral image
unminxing

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10.1109/WHISPERS.2016.8071731

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, California, United States. https://doi.org/10.1109/WHISPERS.2016.8071731

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title = "Exploiting the low-rank property of hyperpsectral imagery: a technical overview",

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 = oct,

day = "19",

doi = "10.1109/WHISPERS.2016.8071731",

language = "English",

pages = "1--4",

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

TY - CONF

T1 - Exploiting the low-rank property of hyperpsectral imagery

T2 - 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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

Y2 - 21 August 2016 through 24 August 2016

ER -

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

Abstract

Workshop

Keywords

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