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Proceedings Paper

Fast unsupervised extraction of endmembers spectra from hyperspectral data
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Paper Abstract

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.

Paper Details

Date Published: 13 February 2004
PDF: 8 pages
Proc. SPIE 5239, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology III, (13 February 2004); doi: 10.1117/12.510663
Show Author Affiliations
Jose M. P. Nascimento, Instituto Superior Engenharia de Lisboa (Portugal)
Instituto de Telecomunicacoes (Portugal)
Jose M. Bioucas Dias, Instituto Superior Tecnico (Portugal)
Instituto de Telecomunicacoes (Portugal)


Published in SPIE Proceedings Vol. 5239:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology III
Manfred Ehlers; Hermann J. Kaufmann; Ulrich Michel, Editor(s)

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