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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
08/07/2013 |
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Data da última atualização: |
22/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
ROMANI, L. A. S.; AMARAL, B. F. do; GONÇALVES, R. R. do V.; ZULLO JÚNIOR, J.; SOUSA, E. P. M. de. |
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Afiliação: |
LUCIANA ALVIM SANTOS ROMANI, CNPTIA; BRUNO FERRAZ DO AMARAL, ICMC/USP; RENATA RIBEIRO DO VALLE GONÇALVES, Cepagri/Unicamp; JURANDIR ZULLO JÚNIOR, Cepagri/Unicamp; ELAINE PARROS MACHADO DE SOUSA, ICMC/USP. |
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Título: |
Aplicação de técnicas de classificação semissupervisionada para análise de séries multitemporais de imagens de satélite. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
In: SIMPOSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 16., 2013, Foz do Iguaçu. Anais... São José dos Campos: INPE, 2013. |
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Páginas: |
p. 1750-1757. |
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ISBN: |
978-85-17-00065-2 |
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Idioma: |
Português |
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Notas: |
SBSR 2013. |
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Conteúdo: |
Este trabalho apresenta uma comparação de dois algoritmos de classificação semissupervisionada utilizados para auxiliar na identificação de áreas de cultivo de cana-de-açúcar, uma importante commoditie brasileira. As técnicas foram incorporadas ao software SatImagExplorer, que foi desenvolvido para auxiliar na extração de séries temporais de imagens de satélite (CHINO; ROMANI; TRAINA, 2010). Os resultados indicam que ambas as técnicas apresentaram resultados satisfatórios para classificação de diferentes classes usando séries de imagens de baixa resolução espacial. |
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Palavras-Chave: |
Image processing; Processamento de imagens. |
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Thesagro: |
Agricultura; Sensoriamento Remoto. |
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Thesaurus Nal: |
Agriculture; Image analysis; Remote sensing. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/85595/1/p1237.pdf
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Marc: |
LEADER 01529nam a2200277 a 4500
001 1961575
005 2020-01-22
008 2013 bl uuuu u00u1 u #d
020 $a978-85-17-00065-2
100 1 $aROMANI, L. A. S.
245 $aAplicação de técnicas de classificação semissupervisionada para análise de séries multitemporais de imagens de satélite.$h[electronic resource]
260 $aIn: SIMPOSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 16., 2013, Foz do Iguaçu. Anais... São José dos Campos: INPE$c2013
300 $ap. 1750-1757.
500 $aSBSR 2013.
520 $aEste trabalho apresenta uma comparação de dois algoritmos de classificação semissupervisionada utilizados para auxiliar na identificação de áreas de cultivo de cana-de-açúcar, uma importante commoditie brasileira. As técnicas foram incorporadas ao software SatImagExplorer, que foi desenvolvido para auxiliar na extração de séries temporais de imagens de satélite (CHINO; ROMANI; TRAINA, 2010). Os resultados indicam que ambas as técnicas apresentaram resultados satisfatórios para classificação de diferentes classes usando séries de imagens de baixa resolução espacial.
650 $aAgriculture
650 $aImage analysis
650 $aRemote sensing
650 $aAgricultura
650 $aSensoriamento Remoto
653 $aImage processing
653 $aProcessamento de imagens
700 1 $aAMARAL, B. F. do
700 1 $aGONÇALVES, R. R. do V.
700 1 $aZULLO JÚNIOR, J.
700 1 $aSOUSA, E. P. M. de
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
07/12/2007 |
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Data da última atualização: |
11/05/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
Internacional - A |
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Autoria: |
OLIVEIRA, S. R. de M.; ALMEIDA, G. V.; SOUZA, K. R. R.; RODRIGUES, D. N.; KUSER-FALCÃO, P. R.; YAMAGISHI, M. E. B.; SANTOS, E. H. dos; VIEIRA, F. D.; JARDINE, J. G.; NESHICH, G. |
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Afiliação: |
STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; PAULA REGINA KUSER FALCAO, CNPTIA; MICHEL EDUARDO BELEZA YAMAGISHI, CNPTIA; EDGARD HENRIQUE DOS SANTOS, CNPTIA; FABIO DANILO VIEIRA, CNPTIA; JOSE GILBERTO JARDINE, CNPTIA; GORAN NESHICH, CNPTIA. |
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Título: |
Sting_RDB: a relational database of structural parameters for protein analysis with support for data warehousing and data mining. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 6, n. 4, p. 911-922, 2007. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract. An effective strategy for managing protein databases is to provide mechanisms to transform raw data into consistent, accurate and reliable information. Such mechanisms will greatly reduce operational inefficiencies and improve one's ability to better handle scientific objectives and interpret the research results. To achieve this challenging goal for the STING project, we introduce Sting_RDB, a relational database of structural parameters for protein analysis with support for data warehousing and data mining. In this article, we highlight the main features of Sting_RDB and show how a user can explore it for efficient and biologically relevant queries. Considering its importance for molecular biologists, effort has been made to advance Sting_RDB toward data quality assessment. To the best of our knowledge, Sting_RDB is one of the most comprehensive data repositories for protein analysis, now also capable of providing its users with a data quality indicator. This paper differs from our previous study in many aspects. First, we introduce Sting_RDB, a relational database with mechanisms for efficient and relevant queries using SQL. Sting_rdb evolved from the earlier, text (flat file)-based database, in which data consistency and integrity was not guaranteed. Second, we provide support for data warehousing and mining. Third, the data quality indicator was introduced. Finally and probably most importantly, complex queries that could not be posed on a text-based database, are now easily implemented. MenosAbstract. An effective strategy for managing protein databases is to provide mechanisms to transform raw data into consistent, accurate and reliable information. Such mechanisms will greatly reduce operational inefficiencies and improve one's ability to better handle scientific objectives and interpret the research results. To achieve this challenging goal for the STING project, we introduce Sting_RDB, a relational database of structural parameters for protein analysis with support for data warehousing and data mining. In this article, we highlight the main features of Sting_RDB and show how a user can explore it for efficient and biologically relevant queries. Considering its importance for molecular biologists, effort has been made to advance Sting_RDB toward data quality assessment. To the best of our knowledge, Sting_RDB is one of the most comprehensive data repositories for protein analysis, now also capable of providing its users with a data quality indicator. This paper differs from our previous study in many aspects. First, we introduce Sting_RDB, a relational database with mechanisms for efficient and relevant queries using SQL. Sting_rdb evolved from the earlier, text (flat file)-based database, in which data consistency and integrity was not guaranteed. Second, we provide support for data warehousing and mining. Third, the data quality indicator was introduced. Finally and probably most importantly, complex queries that could not be posed on a text-based database,... Mostrar Tudo |
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Palavras-Chave: |
Análise de estrutura de proteínas; Base de dados Sting; Bioinformática; Data mining; Data warehousing; Mineração de dados. |
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Thesagro: |
Proteína. |
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Thesaurus NAL: |
Bioinformatics; Databases; Proteins. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/159698/1/AP-Sting-GMR-2007.pdf
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Marc: |
LEADER 02591naa a2200349 a 4500
001 1000814
005 2017-05-11
008 2007 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, S. R. de M.
245 $aSting_RDB$ba relational database of structural parameters for protein analysis with support for data warehousing and data mining.$h[electronic resource]
260 $c2007
520 $aAbstract. An effective strategy for managing protein databases is to provide mechanisms to transform raw data into consistent, accurate and reliable information. Such mechanisms will greatly reduce operational inefficiencies and improve one's ability to better handle scientific objectives and interpret the research results. To achieve this challenging goal for the STING project, we introduce Sting_RDB, a relational database of structural parameters for protein analysis with support for data warehousing and data mining. In this article, we highlight the main features of Sting_RDB and show how a user can explore it for efficient and biologically relevant queries. Considering its importance for molecular biologists, effort has been made to advance Sting_RDB toward data quality assessment. To the best of our knowledge, Sting_RDB is one of the most comprehensive data repositories for protein analysis, now also capable of providing its users with a data quality indicator. This paper differs from our previous study in many aspects. First, we introduce Sting_RDB, a relational database with mechanisms for efficient and relevant queries using SQL. Sting_rdb evolved from the earlier, text (flat file)-based database, in which data consistency and integrity was not guaranteed. Second, we provide support for data warehousing and mining. Third, the data quality indicator was introduced. Finally and probably most importantly, complex queries that could not be posed on a text-based database, are now easily implemented.
650 $aBioinformatics
650 $aDatabases
650 $aProteins
650 $aProteína
653 $aAnálise de estrutura de proteínas
653 $aBase de dados Sting
653 $aBioinformática
653 $aData mining
653 $aData warehousing
653 $aMineração de dados
700 1 $aALMEIDA, G. V.
700 1 $aSOUZA, K. R. R.
700 1 $aRODRIGUES, D. N.
700 1 $aKUSER-FALCÃO, P. R.
700 1 $aYAMAGISHI, M. E. B.
700 1 $aSANTOS, E. H. dos
700 1 $aVIEIRA, F. D.
700 1 $aJARDINE, J. G.
700 1 $aNESHICH, G.
773 $tGenetics and Molecular Research$gv. 6, n. 4, p. 911-922, 2007.
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