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New developments on VCA unmixing algorithm
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Paper Abstract

Hyperspectral sensors are being developed for remote sensing applications. These sensors produce huge data volumes which require faster processing and analysis tools. Vertex component analysis (VCA) has become a very useful tool to unmix hyperspectral data. It has been successfully used to determine endmembers and unmix large hyperspectral data sets without the use of any a priori knowledge of the constituent spectra. Compared with other geometric-based approaches VCA is an efficient method from the computational point of view. In this paper we introduce new developments for VCA: 1) a new signal subspace identification method (HySime) is applied to infer the signal subspace where the data set live. This step also infers the number of endmembers present in the data set; 2) after the projection of the data set onto the signal subspace, the algorithm iteratively projects the data set onto several directions orthogonal to the subspace spanned by the endmembers already determined. The new endmember signature corresponds to these extreme of the projections. The capability of VCA to unmix large hyperspectral scenes (real or simulated), with low computational complexity, is also illustrated.

Paper Details

Date Published: 10 October 2008
PDF: 9 pages
Proc. SPIE 7109, Image and Signal Processing for Remote Sensing XIV, 71090F (10 October 2008); doi: 10.1117/12.799838
Show Author Affiliations
José M. P. Nascimento, Instituto Superior de Engenharia de Lisboa (Portugal)
Instituto de Telecomunicaçóes (Portugal)
José M. Bioucas-Dias, Instituto Superior Técnico (Portugal)
Instituto de Telecomunicaçóes (Portugal)

Published in SPIE Proceedings Vol. 7109:
Image and Signal Processing for Remote Sensing XIV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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