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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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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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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca |
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| Registros recuperados : 24 | |
| 3. |  | AGUIAR NETTO, A. de O.; RODRIGUES, J. D.; PINHO, S. Z. de. Análise de crescimento na cultura da batata submetida a diferentes lâminas de irrigação. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 35, n. 5, p. 901-907, maio, 2000. Título em inglês: Growth analysis in the potato crop under different irrigation levels.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 10. | | MAGALHÃES, L. T. S.; GOMES, J. B. V.; VASCO, A. N. do; AGUIAR NETTO, A. de O.; FERREIRA, R. A. Caracterização geo-pedológica das áreas de nascentes na bacia hidrográfica do rio Piauitinga, Sergipe, Brasil. Revista Ambiente & Água, p. 169-181, v. 7, n. 1, 2012.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 3 |
| Biblioteca(s): Embrapa Florestas. |
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| 11. | | SILVA, T. L. da; AGUIAR NETTO, A. de O.; GONZAGA, M. I. S.; PACHECO, E. P.; LUCAS, A. A. T. Intervalo hídrico ótimo utilizado como indicador da qualidade física do solo em perímetro irrigado. Revista Agrogeoambiental, Pouso Alegre, v. 8, n. 4, p. 71-81, dez. 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 5 |
| Biblioteca(s): Embrapa Tabuleiros Costeiros. |
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| 14. | | BASTOS, E. A.; AGUIAR NETTO, A. de O.; ANDRADE JUNIOR, A. S. de; OLIVEIRA, C. de M. M. de. Distribuição de frequência da evapotranspiração de referência para fins de dimensionamento de sistemas de irrigação na região de Teresina, PI. In: SEMINÁRIO DE PESQUISA AGROPECUÁRIA DO PIAUÍ, 8.; SIMPÓSIO AGROPECUÁRIO E FLORESTAL DO MEIO-NORTE, 1., 1994, Teresina. Pesquisa e desenvolvimento para o Meio-Norte: anais. Teresina: EMBRAPA Meio-Norte; São Luís: EMAPA, 1997. p. 13-18 (EMBRAPA-CPAMN. Documentos, 16).| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Meio-Norte. |
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| 16. | | BARROS, A. C.; COELHO, R. D.; MARIN, F. R.; POLZER, D. L.; AGUIAR NETTO, A. de O. Utilização do modelo canegro para estimativa de crescimento da cana-de-açúcar irrigada e não irrigada para as regiões de Gurupi - TO e Teresina - PI. Irriga, Botucatu, v. 17, n. 2, p. 189-207, abr./jun. 2012.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 2 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 18. | | COELHO, E. F.; OLIVEIRA, A. S. de; AGUIAR NETTO, A. de O.; TEIXEIRA, A. H. de C.; ARAUJO, E. C. E.; BASSOI, L. H. Irrigação. In: GENU, P. J. de C.; PINTO, A. C. de Q. (Ed.). A cultura da mangueira. Brasília, DF: Embrapa Informação Tecnológica, 2002. cap. 9, p. 165-189. il.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Meio-Norte; Embrapa Semiárido. |
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| Registros recuperados : 24 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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