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
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
of Brazilian agriculture.
(More)