Fast Unsupervised Extraction of Endmembers spectra from Hyperspectral Data
Fast Unsupervised Extraction of Endmembers spectra from Hyperspectral Data, Proc SPIE, Barcelona, Spain, Vol. 5239, pp. 314 - 321, September, 2003.
Digital Object Identifier: 10.1117/12.510663
Linear unmixing decomposes an hyperspectral image into a collection of reflectance spectra, called endmember
signatures, and a set corresponding abundance fractions from the respective spatial coverage. This paper introduces vertex component analysis, an unsupervised algorithm to unmix linear mixtures of hyperpsectral data.
VCA exploits the fact that endmembers occupy vertices of a simplex, and assumes the presence of pure pixels
in data. VCA performance is illustrated using simulated and real data. VCA competes with state-of-the-art
methods with much lower computational complexity.