Spectral mixture analysis of EELS spectrum-images

TL;DR

This paper demonstrates how spectral unmixing algorithms, originally developed for remote sensing hyperspectral images, can be effectively applied to analyze electron energy-loss spectroscopy (EELS) spectrum-images, overcoming limitations of traditional statistical methods.

Contribution

The study adapts a spectral unmixing algorithm for hyperspectral images to EELS spectrum-images, showing improved analysis over PCA and ICA methods.

Findings

SU effectively decomposes EELS spectrum-images into constituent spectra and abundances.

SU outperforms PCA and ICA in linear spectral mixture analysis of EELS data.

Example demonstrates the potential of SU for detailed EELS analysis.

Abstract

Recent advances in detectors and computer science have enabled the acquisition and the processing of multidimensional datasets, in particular in the field of spectral imaging. Benefiting from these new developments, earth scientists try to recover the reflectance spectra of macroscopic materials (e.g., water, grass, mineral types...) present in an observed scene and to estimate their respective proportions in each mixed pixel of the acquired image. This task is usually referred to as spectral mixture analysis or spectral unmixing (SU). SU aims at decomposing the measured pixel spectrum into a collection of constituent spectra, called endmembers, and a set of corresponding fractions (abundances) that indicate the proportion of each endmember present in the pixel. Similarly, when processing spectrum-images, microscopists usually try to map elemental, physical and chemical state information of a given material. This paper reports how a SU algorithm dedicated to remote sensing hyperspectral images can be successfully applied to analyze spectrum-image resulting from electron energy-loss spectroscopy (EELS). SU generally overcomes standard limitations inherent to other multivariate statistical analysis methods, such as principal component analysis (PCA) or independent component analysis (ICA), that have been previously used to analyze EELS maps. Indeed, ICA and PCA may perform poorly for linear spectral mixture analysis due to the strong dependence between the abundances of the different materials. One example is presented here to demonstrate the potential of this technique for EELS analysis.