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Endmember extraction from hyperspectral imagery based on QR factorisation using givens rotations
Gan, Yuquan1,2; Hu, Bingliang1; Liu, Weihua1; Wang, Shuang1; Zhang, Geng1; Feng, Xiangpeng1; Wen, Desheng1
Department光谱成像技术实验室
2019
Source PublicationIET IMAGE PROCESSING
ISSN1751-9659;1751-9667
Volume13Issue:2Pages:332-343
Contribution Rank1
Abstract

Hyperspectral images are mixtures of spectra of materials in a scene. Accurate analysis of hyperspectral image requires spectral unmixing. The result of spectral unmixing is the material spectral signatures and their corresponding fractions. The materials are called endmembers. Endmember extraction equals to acquire spectral signatures of the materials. In this study, the authors propose a new hyperspectral endmember extraction algorithm for hyperspectral image based on QR factorisation using Givens rotations (EEGR). Evaluation of the algorithm is demonstrated by comparing its performance with two popular endmember extraction methods, which are vertex component analysis (VCA) and maximum volume by householder transformation (MVHT). Both simulated mixtures and real hyperspectral image are applied to the three algorithms, and the quantitative analysis of them is presented. EEGR exhibits better performance than VCA and MVHT. Moreover, EEGR algorithm is convenient to implement parallel computing for real-time applications based on the hardware features of Givens rotations.

Keywordgeophysical image processing feature extraction hyperspectral imaging spectral analysis hyperspectral imagery QR factorisation givens rotations spectral unmixing material spectral signatures hyperspectral endmember extraction algorithm popular endmember extraction methods EEGR vertex component analysis VCA maximum volume by householder transformation MVHT hardware features
DOI10.1049/iet-ipr.2018.5079
Indexed BySCI ; EI
Language英语
WOS IDWOS:000459477400013
PublisherINST ENGINEERING TECHNOLOGY-IET
EI Accession Number20190906561915
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31168
Collection光谱成像技术实验室
Corresponding AuthorHu, Bingliang
Affiliation1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol, 17 Xinxi Rd, Xian, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, 19 Yuquan Rd, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Gan, Yuquan,Hu, Bingliang,Liu, Weihua,et al. Endmember extraction from hyperspectral imagery based on QR factorisation using givens rotations[J]. IET IMAGE PROCESSING,2019,13(2):332-343.
APA Gan, Yuquan.,Hu, Bingliang.,Liu, Weihua.,Wang, Shuang.,Zhang, Geng.,...&Wen, Desheng.(2019).Endmember extraction from hyperspectral imagery based on QR factorisation using givens rotations.IET IMAGE PROCESSING,13(2),332-343.
MLA Gan, Yuquan,et al."Endmember extraction from hyperspectral imagery based on QR factorisation using givens rotations".IET IMAGE PROCESSING 13.2(2019):332-343.
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