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Dimensionality reduction based on parallel factor analysis model and independent component analysis method
Yan, Ronghua1,2; Peng, Jinye1,3; Ma, Dongmei4
Department空间光学应用研究室
2019-03-29
Source PublicationJOURNAL OF APPLIED REMOTE SENSING
ISSN1931-3195
Volume13Issue:1
Contribution Rank1
Abstract

In hyperspectral image (HSI) analysis, dimensionality reduction is a preprocessing step for HSI classification. Independent component analysis (ICA) reduces the spectral dimension and does not utilize the spatial information of the HSI. To solve it, tensor decompositions have been successfully applied to joint noise reduction in spatial and spectral dimensions of HSIs, such as parallel factor analysis (PARAFAC). However, the PARAFAC method does not reduce the dimension in the spectral dimension. We proposed a method to improve it, which combines ICA 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 this method improves the classification compared with the previous methods. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

Keyworddimensionality reduction tensor processing hyperspectral image parallel factor analysis independent component analysis
DOI10.1117/1.JRS.13.014532
Indexed BySCI ; EI
Language英语
WOS IDWOS:000463292300001
PublisherSPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
EI Accession Number20191606775115
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31366
Collection空间光学应用研究室
Corresponding AuthorYan, Ronghua
Affiliation1.Northwestern Polytech Univ, Sch Elect & Informat, Xian, Shaanxi, Peoples R China
2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China
3.Northwest Univ, Sch Informat & Technol, Xian, Shaanxi, Peoples R China
4.Xian Janssen Pharmaceut Ltd, Xian, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Yan, Ronghua,Peng, Jinye,Ma, Dongmei. Dimensionality reduction based on parallel factor analysis model and independent component analysis method[J]. JOURNAL OF APPLIED REMOTE SENSING,2019,13(1).
APA Yan, Ronghua,Peng, Jinye,&Ma, Dongmei.(2019).Dimensionality reduction based on parallel factor analysis model and independent component analysis method.JOURNAL OF APPLIED REMOTE SENSING,13(1).
MLA Yan, Ronghua,et al."Dimensionality reduction based on parallel factor analysis model and independent component analysis method".JOURNAL OF APPLIED REMOTE SENSING 13.1(2019).
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