  Spatially resolved Electron Energy-Loss Spectroscopy (EELS) conducted in a
Scanning Transmission Electron Microscope (STEM) enables the acquisition of
hyperspectral images (HSIs). Spectral unmixing (SU) is the process of
decomposing each spectrum of an HSI into a combination of representative
spectra (endmembers) corresponding to compounds present in the sample along
with their local proportions (abundances). SU is a complex task, and various
methods have been developed in different communities using HSIs. However, none
of these methods fully satisfy the STEM-EELS requirements. Recent advancements
in remote sensing, which focus on Deep Learning techniques, have the potential
to meet these requirements, particularly Autoencoders (AEs). In this study, the
performance of Deep Learning methods using AE for SU is evaluated, and their
results are compared with traditional methods. Synthetic HSIs have been created
to quantitatively assess the outcomes of the unmixing process using specific
metrics. The methods are subsequently applied to a series of experimental data.
The findings demonstrate the promising potential of AE as a tool for STEM-EELS
SU, marking a starting point for exploring more sophisticated Neural Networks.

Data Analysis, Statistics and Probability;Materials Science