Paper Info
Title
Endmember extraction from hyperspectral imagery based on QR factorisation using givens rotations
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.
Year
DOI
Venue
2019
10.1049/iet-ipr.2018.5079
IET Image Processing
Keywords
Field
DocType
geophysical image processing,feature extraction,hyperspectral imaging,spectral analysis
Endmember,Pattern recognition,Extraction algorithm,Image based,Hyperspectral imaging,Vertex component analysis,Householder transformation,Factorization,Artificial intelligence,Spectral signature,Mathematics
Journal
Volume
Issue
ISSN
13
2
1751-9659
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Yuquan Gan100.34
Bingliang Hu201.01
Weihua Liu331.75
Shuang Wang432.40
Geng Zhang501.35
Xiangpeng Feng600.68
Desheng Wen703.72