  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.
