Open AccessEndmember extraction from hyperspectral imagery based on QR factorisation using givens rotations
Author(s) -
Gan Yuquan,
Hu Bingliang,
Liu Weihua,
Wang Shuang,
Zhang Geng,
Feng Xiangpeng,
Wen Desheng
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5079
Subject(s) - endmember , hyperspectral imaging , computer science , spectral signature , pattern recognition (psychology) , artificial intelligence , extraction (chemistry) , principal component analysis , algorithm , computer vision , remote sensing , geology , chemistry , chromatography
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.