UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Denoising of Hyperspectral Images Using Group Low-Rank Representation

Wang, M; Yu, J; Xue, J-H; Sun, W; (2016) Denoising of Hyperspectral Images Using Group Low-Rank Representation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (9) pp. 4420-4427. 10.1109/JSTARS.2016.2531178. Green open access

[img]
Preview
Text
MengdiWang-JSTARS-2016-UCL.pdf - ["content_typename_Accepted version" not defined]

Download (3MB) | Preview

Abstract

Hyperspectral images (HSIs) have been used in a wide range of fields, such as agriculture, food safety, mineralogy and environment monitoring, but being corrupted by various kinds of noise limits its efficacy. Low-rank representation (LRR) has proved its effectiveness in the denoising of HSIs. However, it just employs local information for denoising, which results in ineffectiveness when local noise is heavy. In this paper, we propose an approach of group low-rank representation (GLRR) for the HSI denoising. In our GLRR, a corrupted HSI is divided into overlapping patches, the similar patches are combined into a group, and the group is reconstructed as a whole using LRR. The proposed method enables the exploitation of both the local similarity within a patch and the nonlocal similarity across the patches in a group simultaneously. The additional nonlocallysimilar patches can bring in extra structural information to the corrupted patches, facilitating the detection of noise as outliers. LRR is applied to the group of patches, as the uncorrupted patches enjoy intrinsic low-rank structure. The effectiveness of the proposed GLRR method is demonstrated qualitatively and quantitatively by using both simulated and real-world data in experiments.

Type: Article
Title: Denoising of Hyperspectral Images Using Group Low-Rank Representation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/JSTARS.2016.2531178
Publisher version: http://dx.doi.org/10.1109/JSTARS.2016.2531178
Language: English
Additional information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Denoising, hyperspectral image, low-rank representation, nonlocal similarity
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/1477657
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item