An overview on hyper-spectral unmixing: geometrical, statistical, and sparse regression based approaches

Hyperspectral instruments acquire electromagnetic energy scattered within their ground instantaneous field view in hun-dreds of spectral channels with high spectral resolution. Very often, however, owing to low spatial resolution of the scan-ner or to the presence of intimate mixtures (mixing of the materials at a very small scale) in the scene, the spectral vec-tors (collection of signals acquired at different spectral bands from a given pixel) acquired by the hyperspectral scanners are actually mixtures of the spectral signatures of the materials present in the scene. Given a set of mixed spectral vectors, spectral mixture analysis (or spectral unmixing) aims at estimating the number of reference materials, also called endmembers, their spectral signatures, and their fractional abundances. Spectral unmix