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Spatially resolved Electron Energy-Loss Spectroscopy (EELS) conducted in aScanning Transmission Electron Microscope (STEM) enables the acquisition ofhyperspectral images (HSIs). Spectral unmixing (SU) is the process ofdecomposing each spectrum of an HSI into a combination of representativespectra (endmembers) corresponding to compounds present in the sample alongwith their local proportions (abundances). SU is a complex task, and variousmethods have been developed in different communities using HSIs. However, noneof these methods fully satisfy the STEM-EELS requirements. Recent advancementsin remote sensing, which focus on Deep Learning techniques, have the potentialto meet these requirements, particularly Autoencoders (AEs). In this study, theperformance of Deep Learning methods using AE for SU is evaluated, and theirresults are compared with traditional methods. Synthetic HSIs have been createdto quantitatively assess the outcomes of the unmixing process using specificmetrics. The methods are subsequently applied to a series of experimental data.The findings demonstrate the promising potential of AE as a tool for STEM-EELSSU, marking a starting point for exploring more sophisticated Neural Networks.

Deep Learning for EELS hyperspectral images unmixing -- using autoencoders