# Endmember Extraction Algorithms Fusing for Hyperspectral Unmixing

arxiv（2024）

Abstract

In recent years, transformer-based deep learning networks have gained
popularity in Hyper-spectral (HS) unmixing applications due to their superior
performance. The attention mechanism within transformers facilitates
input-dependent weighting and enhances contextual awareness during training.
Drawing inspiration from this, we propose a novel attention-based Hyperspectral
Unmixing algorithm called Fusion (Fusion). This algorithm can effectively fuse
endmember signatures obtained from different endmember extraction algorithms,
surpassing the limitations of classical HS Unmixing approaches that rely on a
single Endmember Extraction Algorithm (EEA). The Fusion network incorporates an
Approximation Network (AN), introducing contextual awareness into abundance
prediction by considering neighborhood pixels. Unlike Convolutional Neural
Networks (CNNs), which are constrained by specific kernel shapes, the Fusion
network offers flexibility in choosing any arbitrary configuration of the
neighborhood. We conducted a comparative analysis between the Fusion algorithm
and state-of-the-art algorithms using two popular datasets. Remarkably, Fusion
outperformed other algorithms, achieving the lowest Root Mean Square Error
(RMSE) for abundance predictions and competitive Spectral Angle Distance (SAD)
for signatures associated with each endmember for both datasets.

MoreTranslated text

AI Read Science

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Chat Paper

Summary is being generated by the instructions you defined