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A New Time Series Mining Approach Applied to Multitemporal Remote Sensing Imagery

Abstract : In this paper, we present a novel unsupervised algorithm, called CLimate and rEmote sensing Association patteRns Miner, for mining association patterns on heterogeneous time series from climate and remote sensing data integrated in a remote sensing information system developed to improve the monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing images, an image preprocessing module, a time series extraction module, and time series mining methods. The preprocessing module was projected to perform accurate geometric correction, what is a requirement particularly for land and agriculture applications of satellite images. The time series extraction is accomplished through a graphical interface that allows easy interaction and high flexibility to users. The time series mining method transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in other series within a temporal sliding window. The validation process was achieved with agroclimatic data and NOAA-AVHRR images of sugar cane fields. Results show a correlation between agroclimatic time series and vegetation index images. Rules generated by our new algorithm show the association patterns in different periods of time in each time series, pointing to a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden of dealing with many data charts.
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Contributor : Richard Chbeir Connect in order to contact the contributor
Submitted on : Friday, December 5, 2014 - 6:30:20 PM
Last modification on : Monday, November 7, 2022 - 5:24:33 PM


  • HAL Id : hal-01091691, version 1



Luciana Romani, Ana Heuminski, Daniel Yoshinobu, Jurandir Zullo, Richard Chbeir, et al.. A New Time Series Mining Approach Applied to Multitemporal Remote Sensing Imagery. IEEE Geoscience and Remote Sensing Letters, 2013, Geoscience and Remote Sensing, 51 (1), pp.140-150. ⟨hal-01091691⟩



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