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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
04/12/2014 |
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Data da última atualização: |
08/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
AMARAL, B. F.; CHINO, D. Y. T.; ROMANI, L. A. S.; GONÇALVES, R. R. V.; TRAINA, A. J. M.; SOUSA, E. P. M. |
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Afiliação: |
BRUNO F. AMARAL, ICMC/USP; DANIEL Y. T. CHINO, ICMC/USP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; RENATA R. V. GONÇALVES, Cepagri/Unicamp; AGMA J. M. TRAINA, ICMC/USP; ELAINE P. M. SOUSA, ICMC/USP. |
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Título: |
The SITSMining framework: a data mining approach for satellite image time series. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, 16.; INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING, 9., 2014, Lisbon. Proceedings... [S.l.]: Scitepress, 2014. |
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Páginas: |
p. 225-232. |
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ISBN: |
978-989-758-027-7 |
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Idioma: |
Inglês |
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Notas: |
ICEIS 2014. |
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Conteúdo: |
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. |
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Palavras-Chave: |
Data mining; Imagens de satélite; Mineração de dados; Multivariate time series; Séries temporais multivariadas. |
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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: |
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001 2001711
005 2020-01-08
008 2014 bl uuuu u00u1 u #d
020 $a978-989-758-027-7
100 1 $aAMARAL, B. F.
245 $aThe SITSMining framework$ba data mining approach for satellite image time series.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, 16.; INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING, 9., 2014, Lisbon. Proceedings... [S.l.]: Scitepress$c2014
300 $ap. 225-232.
500 $aICEIS 2014.
520 $aAbstract: 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.
650 $aRemote sensing
650 $aTime series analysis
650 $aSensoriamento Remoto
653 $aData mining
653 $aImagens de satélite
653 $aMineração de dados
653 $aMultivariate time series
653 $aSéries temporais multivariadas
700 1 $aCHINO, D. Y. T.
700 1 $aROMANI, L. A. S.
700 1 $aGONÇALVES, R. R. V.
700 1 $aTRAINA, A. J. M.
700 1 $aSOUSA, E. P. M.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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