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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. |
Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
04/12/2014 |
Data da última atualização: |
08/01/2020 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
AMARAL, B. F.; CHINO, D. Y. T.; ROMANI, L. A. S.; GONÇALVES, R. R. V.; TRAINA, A. J. M.; SOUSA, E. P. M. |
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. |
Título: |
The SITSMining framework: a data mining approach for satellite image time series. |
Ano de publicação: |
2014 |
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. |
Páginas: |
p. 225-232. |
ISBN: |
978-989-758-027-7 |
Idioma: |
Inglês |
Notas: |
ICEIS 2014. |
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. |
Palavras-Chave: |
Data mining; Imagens de satélite; Mineração de dados; Multivariate time series; Séries temporais multivariadas. |
Thesagro: |
Sensoriamento Remoto. |
Thesaurus NAL: |
Remote sensing; Time series analysis. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 02103nam a2200301 a 4500 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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