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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
会议名称International Symposium on Optoelectronic Technology and Application 2018: Optical Sensing and Imaging Technologies and Applications 2018, OTA 2018
会议录名称Optical Sensing and Imaging Technologies and Applications
卷号10846
会议日期2018-05-22
会议地点Beijing, China
出版者SPIE
产权排序1
摘要In 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.
作者部门空间光学应用研究室
DOI10.1117/12.2505370
收录类别EI
ISBN号9781510623347
语种英语
ISSN号0277786X;1996765X
EI入藏号20185206302182
引用统计
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/31123
专题空间光学应用研究室
作者单位1.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
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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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