Data/hora: 21/09/2024 18:20:15

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	19/12/2013	
Data da última atualização: 	08/01/2020	
Autoria: 	ROMANI, L. A. S.; AVILA, A. M. H. de; CHINO, D. Y. T.; ZULLO JÚNIOR, J.; CHBEIR, R.; TRAINA JÚNIOR, C.; TRAINA, A. J. M.	
Afiliação: 	LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ANA MARIA H. DE AVILA, Cepagri/Unicamp; DANIEL Y. T. CHINO, ICMC/USP; JURANDIR ZULLO JÚNIOR, Cepagri/Unicamp; RICHARD CHBEIR, University of Bourgogne; CAETANO TRAINA JÚNIOR, ICMC/USP; AGMA J. M. TRAINA, ICMC/USP.	
Título: 	A new time series mining approach applied to multitemporal remote sensing imagery.	
Ano de publicação: 	2013	
Fonte/Imprenta: 	IEEE transactions on geoscience and remote sensing, New York, v. 51, n. 1, p. 140-150, Jan. 2013.	
Idioma: 	Inglês	
Conteúdo: 	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.	
Palavras-Chave: 	Association rules; Imagens NOAA-AVHRR; Regras de associação; Séries temporais.	
Thesagro: 	Sensoriamento Remoto.	
Thesaurus Nal: 	Remote sensing; Time series analysis.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (17695-1) - UPC - AP - DD -  -  -  - DIS