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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
06/07/2020 |
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Data da última atualização: |
07/07/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SANTOS, M. A. S.; ASSAD, E. D.; GURGEL, A. C.; OMAR, N. |
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Afiliação: |
MARCIO A. S. SANTOS, Mackenzie Presbyterian University; EDUARDO DELGADO ASSAD, CNPTIA; ANGELO C. GURGEL, FGV; NIZAM OMAR, Mackenzie Presbyterian University. |
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Título: |
Similarity metrics enforcement in seasonal agriculture areas classification. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing, v. 12, n. 11, p. 1-14, 2020. |
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DOI: |
https://doi.org/10.3390/rs12111791 |
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Idioma: |
Inglês |
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Notas: |
Article 1791. |
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Conteúdo: |
Abstract. Accurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deliver accurate information. In this paper, a Machine Learning (ML) based method to enhance the classification process of areas dedicated to seasonal crops (row crops) is proposed. To this objective, a broad exploration of data from Moderate Resolution Imaging Spectro-radiometer sensors (MODIS) was made using pixel time-series combined with time-series similarity metrics. The experiment was performed in Brazil, covered 61% of the total agriculture areas, five different states specifically selected to demonstrate biome differences and the country´s diversity. The validation was made against independent data from EMBRAPA (Brazilian Agriculture Research Corporation), RapidEye Sensor Scene Maps. For the eight tested algorithms, the results were enhanced and demonstrate that the method can rate the classification accuracy up to 98.5%, average value for the tested algorithms. The process can be used to timely monitor large areas dedicated to row crops and enables the application of state of art classification techniques, two levels classification process, to identify crops according to each specific need within the areas. |
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Palavras-Chave: |
Aprendizado de máquina; Dinâmica de uso da terra; Land use dynamics; Time series similarity metrics. |
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Thesagro: |
Agricultura; Sensoriamento Remoto; Uso da Terra. |
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Thesaurus Nal: |
Agriculture; Land use; Remote sensing; Time series analysis. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/214380/1/AP-Similarity-metrics-2020.pdf
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Marc: |
LEADER 02359naa a2200313 a 4500
001 2123627
005 2020-07-07
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/rs12111791$2DOI
100 1 $aSANTOS, M. A. S.
245 $aSimilarity metrics enforcement in seasonal agriculture areas classification.$h[electronic resource]
260 $c2020
500 $aArticle 1791.
520 $aAbstract. Accurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deliver accurate information. In this paper, a Machine Learning (ML) based method to enhance the classification process of areas dedicated to seasonal crops (row crops) is proposed. To this objective, a broad exploration of data from Moderate Resolution Imaging Spectro-radiometer sensors (MODIS) was made using pixel time-series combined with time-series similarity metrics. The experiment was performed in Brazil, covered 61% of the total agriculture areas, five different states specifically selected to demonstrate biome differences and the country´s diversity. The validation was made against independent data from EMBRAPA (Brazilian Agriculture Research Corporation), RapidEye Sensor Scene Maps. For the eight tested algorithms, the results were enhanced and demonstrate that the method can rate the classification accuracy up to 98.5%, average value for the tested algorithms. The process can be used to timely monitor large areas dedicated to row crops and enables the application of state of art classification techniques, two levels classification process, to identify crops according to each specific need within the areas.
650 $aAgriculture
650 $aLand use
650 $aRemote sensing
650 $aTime series analysis
650 $aAgricultura
650 $aSensoriamento Remoto
650 $aUso da Terra
653 $aAprendizado de máquina
653 $aDinâmica de uso da terra
653 $aLand use dynamics
653 $aTime series similarity metrics
700 1 $aASSAD, E. D.
700 1 $aGURGEL, A. C.
700 1 $aOMAR, N.
773 $tRemote Sensing$gv. 12, n. 11, p. 1-14, 2020.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
21/02/2005 |
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Data da última atualização: |
21/02/2005 |
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Autoria: |
SILVA, H. T. da; ANTUNES, I. F. |
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Título: |
Caracterização da vagem de feijão (Phaseolus vulgaris L) : Definição de classes de comprimento e largura. |
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Ano de publicação: |
2002 |
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Fonte/Imprenta: |
In: CONGRESSO NACIONAL DE PESQUISA DE FEIJÃO, 7., 2002, Viçosa, MG. Resumos expandidos. Viçosa: UFV; DFT, 2002. |
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Páginas: |
p. 74-75. |
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Idioma: |
Português |
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Palavras-Chave: |
Caracterização. |
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Thesagro: |
Feijão; Phaseolus Vulgaris; Vagem. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00603naa a2200181 a 4500
001 1192281
005 2005-02-21
008 2002 bl uuuu u00u1 u #d
100 1 $aSILVA, H. T. da
245 $aCaracterização da vagem de feijão (Phaseolus vulgaris L)$bDefinição de classes de comprimento e largura.
260 $c2002
300 $ap. 74-75.
650 $aFeijão
650 $aPhaseolus Vulgaris
650 $aVagem
653 $aCaracterização
700 1 $aANTUNES, I. F.
773 $tIn: CONGRESSO NACIONAL DE PESQUISA DE FEIJÃO, 7., 2002, Viçosa, MG. Resumos expandidos. Viçosa: UFV; DFT, 2002.
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Embrapa Arroz e Feijão (CNPAF) |
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