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Authors: Goo Jun 1 ; Joydeep Ghosh 1 ; Vladan Radosavljevic 2 and Zoran Obradovic 2

Affiliations: 1 University of Texas, United States ; 2 Temple University, United States

ISBN: 978-989-8425-28-7

Keyword(s): Aerosol, AERONET, MODIS, Gaussian Process, Active Learning, Spatio-temporal Data Mining.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Interactive and Online Data Mining ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Structured Data Analysis and Statistical Methods ; Symbolic Systems

Abstract: A Gaussian process regression technique is proposed to predict ground-based aerosol optical depth measurements from satellite multispectral images, and to select the most informative ground-based sites by active learning. Satellite images provide spatial and temporal information in addition to the spectral features, and such heterogeneity of available features is captured in the Gaussian process model by employing an additive set of covariance functions. By finding an optimal set of hyperparameters, relevance of each additional information is automatically determined. Experiments show that the spatio-temporal information contributes significantly to the regression results. The prediction results are not only more accurate but also more interpretable than existing approaches. For active learning, each spatio-temporal setup is evaluated by an uncertainty-sampling algorithm. The results show that the active selection process benefits most from the spatial information.

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Paper citation in several formats:
Jun, G.; Ghosh, J.; Radosavljevic, V. and Obradovic, Z. (2010). PREDICTING GROUND-BASED AEROSOL OPTICAL DEPTH WITH SATELLITE IMAGES VIA GAUSSIAN PROCESSES.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 370-375. DOI: 10.5220/0003115203700375

@conference{kdir10,
author={Goo Jun. and Joydeep Ghosh. and Vladan Radosavljevic. and Zoran Obradovic.},
title={PREDICTING GROUND-BASED AEROSOL OPTICAL DEPTH WITH SATELLITE IMAGES VIA GAUSSIAN PROCESSES},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={370-375},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003115203700375},
isbn={978-989-8425-28-7},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - PREDICTING GROUND-BASED AEROSOL OPTICAL DEPTH WITH SATELLITE IMAGES VIA GAUSSIAN PROCESSES
SN - 978-989-8425-28-7
AU - Jun, G.
AU - Ghosh, J.
AU - Radosavljevic, V.
AU - Obradovic, Z.
PY - 2010
SP - 370
EP - 375
DO - 10.5220/0003115203700375

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