Noise reduction of hyperspectral imagery based on hypergraph laplacian regularized low-rank representation
Authors: 
Xue Zhixiang, Yu Xuchu, Zhou YawenAuthors Info & Claims
Article No.: 42, Pages 1 - 6
Abstract
Low-rank representation is one of the state-of-art hyperspectral image denoising algorithms, but it suffers from ignoring the high-order relationships between data points in image. In this paper, we propose a hypergraph laplacian regularized low-rank representation algorithm for noise reduction of hyperspectral images, which can represent the high-order relations between data points by using the hypergraph laplacian regularization. On the other hand, to further improve the ability to maintain the local information, the sparse and non-negative constraints have been added to the model coefficient matrix. The proposed method not only resumes the low-rank signal components, but also represents the high-order relations of the image data. The experimental results on AVIRIS and ProSpecTIR-VS datasets show that the proposed approach canbetter maintain the spatial and spectral information of images, which improves the hyperspectral image denoising results.
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- Noise reduction of hyperspectral imagery based on hypergraph laplacian regularized low-rank representation
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Published In
December 2016
358 pages
ISBN:9781450347990
DOI:10.1145/3028842
Copyright © 2016 ACM.
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- Jilin Institute of Chemical Technology: Jilin Institute of Chemical Technology, Jilin, China
- Wanfang Data: Wanfang Data, Beijing, China
- CNKI: CNKI, Beijing, China
- Airiti: Airiti, Taiwan
- Guilin: Guilin University of Technology, Guilin, China
- Wuhan University of Technology: Wuhan University of Technology, Wuhan, China
- Ain Shams University: Ain Shams University, Egypt
- International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 December 2016
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ICIIP 2016
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- Jilin Institute of Chemical Technology
- Wanfang Data
- CNKI
- Airiti
- Guilin
- Wuhan University of Technology
- Ain Shams University
- International Engineering and Technology Institute, Hong Kong
ICIIP 2016: 2016 International Conference on Intelligent Information Processing
December 23 - 25, 2016
Wuhan, China
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ICIIP '16 Paper Acceptance Rate 55 of 165 submissions, 33%;
Overall Acceptance Rate 87 of 367 submissions, 24%
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