Similarity metrics enforcement in seasonal agriculture areas classification.
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123627 https://doi.org/10.3390/rs12111791 |
Resumo: | 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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Similarity metrics enforcement in seasonal agriculture areas classification.Aprendizado de máquinaDinâmica de uso da terraTime series similarity metricsLand use dynamicsAgriculturaSensoriamento RemotoUso da TerraAgricultureRemote sensingLand useTime series analysisAbstract. 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.Article 1791.MARCIO A. S. SANTOS, Mackenzie Presbyterian University; EDUARDO DELGADO ASSAD, CNPTIA; ANGELO C. GURGEL, FGV; NIZAM OMAR, Mackenzie Presbyterian University.SANTOS, M. A. S.ASSAD, E. D.GURGEL, A. C.OMAR, N.2020-07-07T11:10:53Z2020-07-07T11:10:53Z2020-07-062020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleRemote Sensing, v. 12, n. 11, p. 1-14, 2020.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123627https://doi.org/10.3390/rs12111791enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2020-07-07T11:11:00Zoai:www.alice.cnptia.embrapa.br:doc/1123627Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542020-07-07T11:11falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542020-07-07T11:11Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Similarity metrics enforcement in seasonal agriculture areas classification. |
title |
Similarity metrics enforcement in seasonal agriculture areas classification. |
spellingShingle |
Similarity metrics enforcement in seasonal agriculture areas classification. SANTOS, M. A. S. Aprendizado de máquina Dinâmica de uso da terra Time series similarity metrics Land use dynamics Agricultura Sensoriamento Remoto Uso da Terra Agriculture Remote sensing Land use Time series analysis |
title_short |
Similarity metrics enforcement in seasonal agriculture areas classification. |
title_full |
Similarity metrics enforcement in seasonal agriculture areas classification. |
title_fullStr |
Similarity metrics enforcement in seasonal agriculture areas classification. |
title_full_unstemmed |
Similarity metrics enforcement in seasonal agriculture areas classification. |
title_sort |
Similarity metrics enforcement in seasonal agriculture areas classification. |
author |
SANTOS, M. A. S. |
author_facet |
SANTOS, M. A. S. ASSAD, E. D. GURGEL, A. C. OMAR, N. |
author_role |
author |
author2 |
ASSAD, E. D. GURGEL, A. C. OMAR, N. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
MARCIO A. S. SANTOS, Mackenzie Presbyterian University; EDUARDO DELGADO ASSAD, CNPTIA; ANGELO C. GURGEL, FGV; NIZAM OMAR, Mackenzie Presbyterian University. |
dc.contributor.author.fl_str_mv |
SANTOS, M. A. S. ASSAD, E. D. GURGEL, A. C. OMAR, N. |
dc.subject.por.fl_str_mv |
Aprendizado de máquina Dinâmica de uso da terra Time series similarity metrics Land use dynamics Agricultura Sensoriamento Remoto Uso da Terra Agriculture Remote sensing Land use Time series analysis |
topic |
Aprendizado de máquina Dinâmica de uso da terra Time series similarity metrics Land use dynamics Agricultura Sensoriamento Remoto Uso da Terra Agriculture Remote sensing Land use Time series analysis |
description |
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. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-07-07T11:10:53Z 2020-07-07T11:10:53Z 2020-07-06 2020 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Remote Sensing, v. 12, n. 11, p. 1-14, 2020. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123627 https://doi.org/10.3390/rs12111791 |
identifier_str_mv |
Remote Sensing, v. 12, n. 11, p. 1-14, 2020. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123627 https://doi.org/10.3390/rs12111791 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503493759795200 |