A new maximum simplex volume method based on Householder Transformation for endmember extraction
发布时间：2025-04-30
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- 发布时间：
- 2025-04-30
- 论文名称：
- A new maximum simplex volume method based on Householder Transformation for endmember extraction
- 发表刊物：
- IEEE Transactions on Geoscience and Remote Sensing
- 摘要：
- 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.
- 合写作者：
- Junmin Liu and Jiangshe Zhang
- 卷号：
- 50(1)
- 是否译文：
- 否
- 发表时间：
- 2012-10-04