Linear and Nonlinear Unmixing in Hyperspectral Imaging

Mainly due to the limited spatial resolution of the data acquisition devices, hyperspectral image pixels generally result from the mixture of several components that are present in the observed surface. Spectral mixture analysis (or spectral unmixing) is a key processing step which aims at identifying the spectral signatures of these materials and quantifying their spatial distribution over the image. The main purpose of this chapter is to introduce the spectral unmixing problem and to discuss some linear and nonlinear models and algorithms used to solve it. We will show that, capitalizing on several decades of methodological developments in the geoscience and remote sensing community, most of the unmixing algorithms proposed to unmix remotely sensed images can be directly applied in the chemometrics field to process hyperspectral data arising from various scanning microscopic techniques such as scanning transmission electron microscopy and Raman imaging.