Date:

2025-04-30

Title of Paper:

A new maximum simplex volume method based on Householder Transformation for endmember extraction

Journal:

IEEE Transactions on Geoscience and Remote Sensing

Summary:

Endmember extraction is very important in hyperspectral
image analysis. The accurate identification of endmembers
enables target detection, classification and efficient
spectral unmixing. Although a number of endmember extraction
algorithms have been proposed, such as two state-of-theart
algorithms—vertex component analysis (VCA) and simplex
growing algorithm (SGA), it is still a rather challenging task. In
this paper, a new maximum simplex volume method based on
Householder transformation, referred to as maximum volume
by Householder transformation (MVHT), is presented for endmember
extraction. The proposed algorithm provides consistent
results with low computational complexity, which overcomes
the disadvantage of the inconsistent result of VCA and the
shortcoming of the high computational cost of SGA resulted
from calculating the simplex volume. A comparative study and
analysis are conducted among the three endmember extraction
algorithms, VCA, SGA and MVHT on both simulated and real
hyperspectral data. The obtained experimental results demonstrate
that the proposed MVHT algorithm generally provides a
competitive or even better performance over VCA and SGA.

Co-author:

Junmin Liu and Jiangshe Zhang

Volume:

50(1)

Translation or Not:

No

Date of Publication:

2012-10-04