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Denoising and dimensionality reduction based on PARAFAC decomposition for hyperspectral images
Yan, Rong-Hua1,2; Peng, Jin-Ye1,3; Wen, De-Sheng2; Ma, Dong-Mei4
2018
Conference NameInternational Symposium on Optoelectronic Technology and Application 2018: Optical Sensing and Imaging Technologies and Applications 2018, OTA 2018
Source PublicationOptical Sensing and Imaging Technologies and Applications
Volume10846
Conference Date2018-05-22
Conference PlaceBeijing, China
PublisherSPIE
Contribution Rank1
AbstractIn hyperspectral image analysis, classification requires spectral dimensionality reduction (DR). Tensor decompositions have been successfully applied to joint noise reduction in spatial and spectral dimensions of hyperspectral images, such as parallel factor analysis (PARAFAC). However, the PARAFAC method does not reduce the dimension in the spectral dimension. To improve itï1/4a new method was proposed in this paper, that is, combine PCA and PARAFAC to reduce both the dimension in the spectral dimension and the noise in the spatial and spectral dimensions. The experiment results indicate that the new method improves the classification compared with the previous methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
Department空间光学应用研究室
DOI10.1117/12.2505370
Indexed ByEI
ISBN9781510623347
Language英语
ISSN0277786X;1996765X
EI Accession Number20185206302182
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31123
Collection空间光学应用研究室
Affiliation1.School of Electronics and Information, Northwestern Polytechnical University, Xi'an; 710072, China;
2.Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China;
3.School of Information and Technology, Northwest University, Xi'an; 710127, China;
4.Xi'an-Janssen Pharmaceutical Ltd., Xi'an; 710043, China
Recommended Citation
GB/T 7714
Yan, Rong-Hua,Peng, Jin-Ye,Wen, De-Sheng,et al. Denoising and dimensionality reduction based on PARAFAC decomposition for hyperspectral images[C]:SPIE,2018.
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