https://doi.org/10.1051/epjap/2024240025
Original Article
EELS hyperspectral images unmixing using autoencoders
Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, 91405 Orsay, France
* e-mail: nathalie.brun@universite-paris-saclay.fr
Received:
20
February
2024
Accepted:
17
September
2024
Published online: 23 October 2024
Spatially resolved Electron Energy-Loss Spectroscopy conducted in a Scanning Transmission Electron Microscope enables the acquisition of hyperspectral images. Spectral unmixing is the process of decomposing each spectrum of a hyperspectral image into a combination of representative spectra (endmembers) corresponding to compounds present in the sample along with their local proportions (abundances). Spectral unmixing is a complex task, and various methods have been developed in different communities using hyperspectral images. However, none of these methods fully satisfy the spatially resolved Electron Energy-Loss Spectroscopy requirements. Recent advancements in remote sensing, which focus on Deep Learning techniques, have the potential to meet these requirements, particularly Autoencoders. As the Neural Networks used are usually shallow it would be more appropriate to use the term “representation learning”. In this study, the performance of these methods using autoencoders for spectral unmixing is evaluated, and their results are compared with traditional methods. Synthetic hyperspectral images 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 autoencoders as a tool for Electron Energy-Loss Spectroscopy hyperspectral images unmixing, marking a starting point for exploring more sophisticated Neural Networks.
Key words: Electron energy-loss spectroscopy (EELS) / spectral imaging / spectrum image / hyperspectral unmixing / autoencoder / deep learning
© N. Brun et al., Published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.