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Spectral Non-local Restoration of Hyperspectral Images with Low-rank Property

Zhu, R; Dong, M; Xue, J; (2015) Spectral Non-local Restoration of Hyperspectral Images with Low-rank Property. Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 8 (6) 3062 -3067. 10.1109/JSTARS.2014.2370062. Green open access

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Abstract

Restoration is important in pre-processing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral non-local approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain pre-cleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.

Type: Article
Title: Spectral Non-local Restoration of Hyperspectral Images with Low-rank Property
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/JSTARS.2014.2370062
Publisher version: http://dx.doi.org/10.1109/JSTARS.2014.2370062
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
Keywords: Hyperspectral image, low rank, non-local means, restoration, spectral and spatial information
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/1456183
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