HYPERSPECTRAL UNMIXING WITH SIMULTANEOUS DIMENSIONALITY ESTIMATION

Jose M. P. Nascimento, José M. Bioucas-Dias

Abstract

This paper is an elaboration of the simplex identification via split augmented Lagrangian (SISAL) algorithm (Bioucas-Dias, 2009) to blindly unmix hyperspectral data. SISAL is a linear hyperspectral unmixing method of the minimum volume class. This method solve a non-convex problem by a sequence of augmented Lagrangian optimizations, where the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. With respect to SISAL, we introduce a dimensionality estimation method based on the minimum description length (MDL) principle. The effectiveness of the proposed algorithm is illustrated with simulated and real data.

References

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Paper Citation


in Harvard Style

M. P. Nascimento J. and M. Bioucas-Dias J. (2012). HYPERSPECTRAL UNMIXING WITH SIMULTANEOUS DIMENSIONALITY ESTIMATION . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: PRARSHIA, (ICPRAM 2012) ISBN 978-989-8425-98-0, pages 438-444. DOI: 10.5220/0003877504380444


in Bibtex Style

@conference{prarshia12,
author={Jose M. P. Nascimento and José M. Bioucas-Dias},
title={HYPERSPECTRAL UNMIXING WITH SIMULTANEOUS DIMENSIONALITY ESTIMATION},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: PRARSHIA, (ICPRAM 2012)},
year={2012},
pages={438-444},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003877504380444},
isbn={978-989-8425-98-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: PRARSHIA, (ICPRAM 2012)
TI - HYPERSPECTRAL UNMIXING WITH SIMULTANEOUS DIMENSIONALITY ESTIMATION
SN - 978-989-8425-98-0
AU - M. P. Nascimento J.
AU - M. Bioucas-Dias J.
PY - 2012
SP - 438
EP - 444
DO - 10.5220/0003877504380444