RETRIEVING AEROSOL CHARACTERISTICS FROM SATELLITE OCEAN COLOR MULTI-SPECTRAL SENSORS USING A NEURAL-VARIATIONAL METHOD

D. Diouf; S. Thiria; A. Niang; J. Brajard; M. Crepon

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Paper

RETRIEVING AEROSOL CHARACTERISTICS FROM SATELLITE OCEAN COLOR MULTI-SPECTRAL SENSORS USING A NEURAL-VARIATIONAL METHOD

Topics: Neural Network Software and Applications; Supervised and Unsupervised Learning

In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 0IJCCI, 297-303, 2011 , Paris, France

Authors: D. Diouf ¹ ; S. Thiria ² ; A. Niang ¹ ; J. Brajard ² and M. Crepon ²

Affiliations: ¹ Ecole Supérieure Polytechnique and Université Cheikh Anta Diop de Dakar, Senegal ; ² Université Paris 6, France

Keyword(s): Multi-layer perceptrons, Atmospheric correction, Variational inversion.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: We present a new algorithm suitable for retrieving and monitoring Saharan dusts from satellite ocean-color multi-spectral observations. This algorithm comprises two steps. The first step consists in classifying the TOA spectra using a neuronal classifier, which provides the aerosol type and a first guess value of the aerosol parameters. The second step retrieves accurate aerosol parameters by using a variational optimization method. We have analyzed 13 years of SeaWiFS images (September 1997-December 2009) in an Atlantic Ocean area off the coast of West Africa. As the method takes into account Saharan dusts, the number of pixels processed is an order of magnitude higher than that processed by the standard SeaWiFS algorithm. We note a strong seasonal variability. The Saharan dust concentration is maximal in summer during the rainy season and minimal in autumn when the vegetation bloom due to the rainy season prevents soil erosion by the wind.

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Paper citation in several formats:

Diouf, D.; Thiria, S.; Niang, A.; Brajard, J. and Crepon, M. (2011). RETRIEVING AEROSOL CHARACTERISTICS FROM SATELLITE OCEAN COLOR MULTI-SPECTRAL SENSORS USING A NEURAL-VARIATIONAL METHOD. In Proceedings of the International Conference on Neural Computation Theory and Applications (IJCCI 2011) - NCTA; ISBN 978-989-8425-84-3, SciTePress, pages 297-303. DOI: 10.5220/0003638802970303

@conference{ncta11,
author={D. Diouf. and S. Thiria. and A. Niang. and J. Brajard. and M. Crepon.},
title={RETRIEVING AEROSOL CHARACTERISTICS FROM SATELLITE OCEAN COLOR MULTI-SPECTRAL SENSORS USING A NEURAL-VARIATIONAL METHOD},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications (IJCCI 2011) - NCTA},
year={2011},
pages={297-303},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003638802970303},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications (IJCCI 2011) - NCTA
TI - RETRIEVING AEROSOL CHARACTERISTICS FROM SATELLITE OCEAN COLOR MULTI-SPECTRAL SENSORS USING A NEURAL-VARIATIONAL METHOD
SN - 978-989-8425-84-3
AU - Diouf, D.
AU - Thiria, S.
AU - Niang, A.
AU - Brajard, J.
AU - Crepon, M.
PY - 2011
SP - 297
EP - 303
DO - 10.5220/0003638802970303
PB - SciTePress