Data/hora: 16/06/2024 06:03:33

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 04/12/2014
Data da última atualização: | 08/01/2020
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
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (18083-1) - UPC - AA - DD -  -  -  - DIS