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Authors: Bruno F. Amaral 1 ; Daniel Y. T. Chino 1 ; Luciana A. S. Romani 2 ; Renata R. V. Gonçalves 3 ; Agma J. M. Traina 1 and Elaine P. M. Sousa 1

Affiliations: 1 University of São Paulo, Brazil ; 2 Embrapa Agricultural Informatics, Brazil ; 3 University of Campinas, Brazil

ISBN: 978-989-758-027-7

Keyword(s): Data Mining, Multivariate Time Series, Remote Sensing, Satellite Image Time Series.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: The amount of data generated and stored in many domains has increased in the last years. In remote sensing, this scenario of bursting data is not different. As the volume of satellite images stored in databases grows, the demand for computational algorithms that can handle and analyze this volume of data and extract useful patterns has increased. In this context, the computational support for satellite images data analysis becomes essential. In this work, we present the SITSMining framework, which applies a methodology based on data mining techniques to extract patterns and information from time series obtained from satellite images. In Brazil, as the agricultural production provides great part of the national resources, the analysis of satellite images is a valuable way to help crops monitoring over seasons, which is an important task to the economy of the country. Thus, we apply the framework to analyze multitemporal satellite images, aiming to help crop monitoring and forecasting o f Brazilian agriculture. (More)

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Paper citation in several formats:
F. Amaral B., Y. T. Chino D., A. S. Romani L., R. V. Gonçalves R., J. M. Traina A. and P. M. Sousa E. (2014). The SITSMining Framework - A Data Mining Approach for Satellite Image Time Series.In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-027-7, pages 225-232. DOI: 10.5220/0004894002250232

@conference{iceis14,
author={Bruno F. Amaral and Daniel Y. T. Chino and Luciana A. S. Romani and Renata R. V. Gonçalves and Agma J. M. Traina and Elaine P. M. Sousa},
title={The SITSMining Framework - A Data Mining Approach for Satellite Image Time Series},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2014},
pages={225-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004894002250232},
isbn={978-989-758-027-7},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - The SITSMining Framework - A Data Mining Approach for Satellite Image Time Series
SN - 978-989-758-027-7
AU - F. Amaral B.
AU - Y. T. Chino D.
AU - A. S. Romani L.
AU - R. V. Gonçalves R.
AU - J. M. Traina A.
AU - P. M. Sousa E.
PY - 2014
SP - 225
EP - 232
DO - 10.5220/0004894002250232

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