Abstract

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 an 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. In this study, the performance of Deep Learning 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.