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.
Original languageEnglish
Pages1-4
Number of pages4
DOIs
Publication statusPublished - 19 Oct 2017
Event2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Los Angeles, United States
Duration: 21 Aug 201624 Aug 2016

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

Keywords

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

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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. 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