Dimensionality Reduction Based on PARAFAC Model | |
Yan, Ronghua2,3; Peng, Jinye1; Ma, Dongmei4 | |
刊名 | JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY |
2019-11 | |
卷号 | 63期号:6 |
ISSN号 | 1062-3701;1943-3522 |
DOI | 10.2352/J.ImagingSci.Technol.2019.63.6.060501 |
产权排序 | 1 |
英文摘要 | In hyperspectral image analysis, dimensionality reduction is a preprocessing step for hyperspectral image (HSI) classification. Principal component analysis (PCA) reduces the spectral dimension and does not utilize the spatial information of an HSI. To solve it, the 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 it, two new methods were proposed in this article, 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 experimental results indicate that the new methods improve the classification compared with the PARAFAC method. (C) 2019 Society for Imaging Science and Technology. |
语种 | 英语 |
出版者 | I S & T-SOC IMAGING SCIENCE TECHNOLOGY |
WOS记录号 | WOS:000508022200016 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/93190] |
专题 | 西安光学精密机械研究所_空间光学应用研究室 |
通讯作者 | Yan, Ronghua |
作者单位 | 1.Northwest Univ, Sch Informat & Technol, Xian 710127, Peoples R China 2.Chinese Acad Sci, Space Opt Lab, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China 3.Northwestern Polytech Univ, Sch Elect & Informat, Xian 710119, Peoples R China 4.Xian Janssen Pharmaceut Ltd, Xian 710043, Peoples R China |
推荐引用方式 GB/T 7714 | Yan, Ronghua,Peng, Jinye,Ma, Dongmei. Dimensionality Reduction Based on PARAFAC Model[J]. JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY,2019,63(6). |
APA | Yan, Ronghua,Peng, Jinye,&Ma, Dongmei.(2019).Dimensionality Reduction Based on PARAFAC Model.JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY,63(6). |
MLA | Yan, Ronghua,et al."Dimensionality Reduction Based on PARAFAC Model".JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY 63.6(2019). |
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