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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
19/12/2013 |
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Data da última atualização: |
08/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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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. |
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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. |
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Título: |
A new time series mining approach applied to multitemporal remote sensing imagery. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
IEEE transactions on geoscience and remote sensing, New York, v. 51, n. 1, p. 140-150, Jan. 2013. |
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Idioma: |
Inglês |
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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. MenosAbstract-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 speci... Mostrar Tudo |
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Palavras-Chave: |
Association rules; Imagens NOAA-AVHRR; Regras de associação; Séries temporais. |
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Thesagro: |
Sensoriamento Remoto. |
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Thesaurus Nal: |
Remote sensing; Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02454naa a2200277 a 4500
001 1974345
005 2020-01-08
008 2013 bl uuuu u00u1 u #d
100 1 $aROMANI, L. A. S.
245 $aA new time series mining approach applied to multitemporal remote sensing imagery.$h[electronic resource]
260 $c2013
520 $aAbstract-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.
650 $aRemote sensing
650 $aTime series analysis
650 $aSensoriamento Remoto
653 $aAssociation rules
653 $aImagens NOAA-AVHRR
653 $aRegras de associação
653 $aSéries temporais
700 1 $aAVILA, A. M. H. de
700 1 $aCHINO, D. Y. T.
700 1 $aZULLO JÚNIOR, J.
700 1 $aCHBEIR, R.
700 1 $aTRAINA JÚNIOR, C.
700 1 $aTRAINA, A. J. M.
773 $tIEEE transactions on geoscience and remote sensing, New York$gv. 51, n. 1, p. 140-150, Jan. 2013.
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| 1. |  | HUSZAR, P.; PETERMANN, P.; LEITE, A.; RESENDE, E. K.; SCHNACK, E.; SCHNEIDER, E.; FRANCESCO, F. O. de; RAST, G.; SCHNACK, J. A.; WASSON, J.; GARCIA LOZANO, L. C.; DANTAS, M.; OBRDLIK, P.; PEDRONI, R. M. Fact or fiction: a review of the Hydrovia Paraguay-Parana official studies. Toronto, Canada: World Wildlife Fund / World Wide Fund for Nature, 1999. 217p.il.| Biblioteca(s): Embrapa Pantanal. |
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