CLC number: TP75
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-02-23
Cited: 0
Clicked: 6679
Citations: Bibtex RefMan EndNote GB/T7714
Jing Li, Xiao-run Li, Li-jiao Wang, Liao-ying Zhao. Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember extraction[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(3): 250-257.
@article{title="Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember extraction",
author="Jing Li, Xiao-run Li, Li-jiao Wang, Liao-ying Zhao",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="3",
pages="250-257",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500244"
}
%0 Journal Article
%T Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember extraction
%A Jing Li
%A Xiao-run Li
%A Li-jiao Wang
%A Liao-ying Zhao
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 3
%P 250-257
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500244
TY - JOUR
T1 - Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember extraction
A1 - Jing Li
A1 - Xiao-run Li
A1 - Li-jiao Wang
A1 - Liao-ying Zhao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 3
SP - 250
EP - 257
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500244
Abstract: endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm (KNSGA), recently developed as a nonlinear alternative to the simplex growing algorithm (SGA), has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index (SPPI) method is used in this study to extract the first endmember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky factorization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tautologically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.
In this paper, the authors improve KNSGA in two aspects: (1) use SPPI to solve the inconsistency in result, and (2) using Cholesky factorization to reduce the computing time. The paper is well organized.
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