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Registro Completo |
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Biblioteca(s): |
Embrapa Territorial; Embrapa Unidades Centrais. |
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Data corrente: |
22/11/2012 |
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Data da última atualização: |
28/10/2014 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LU, D.; BATISTELLA, M.; LI, G.; MORAN, E.; HETRICK, S.; FREITAS, C. DA C.; SANT'ANNA, S. J. |
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Afiliação: |
DENGSHENG LU, INDIANA UNIVERSITY; MATEUS BATISTELLA, CNPM; GUIYING LI, INDIANA UNIVERSITY; EMILIO MORAN, INDIANA UNIVERSITY; SCOTT HETRICK, INDIANA UNIVERSITY; CORINA DA COSTA FREITAS, INPE; SIDNEI JOÃO SIQUEIRA SANT'ANNA, INPE. |
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Título: |
Land use/cover classification in the Brazilian Amazon using satellite images. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasilia, DF, v. 47, n. 9, p. 1185-1208, set. 2012. |
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Páginas: |
p. 1185-1208. |
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DOI: |
dx.doi.org/10.1590/S0100-204X2012000900004 |
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Idioma: |
Inglês |
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Conteúdo: |
Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation?based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi?resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical?based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. MenosLand use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation?based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi?resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical?based methods is fundamental f... Mostrar Tudo |
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Palavras-Chave: |
Classificador não paramétrico; Dado de sensor múltiplo; Data fusion; Fusão de dados; Multiple sensor data; Nonparametric classifiers. |
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Thesagro: |
Textura. |
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Thesaurus Nal: |
Texture. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/70627/1/BatistellaPAB.pdf
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Marc: |
LEADER 02522naa a2200313 a 4500
001 1940299
005 2014-10-28
008 2012 bl uuuu u00u1 u #d
024 7 $adx.doi.org/10.1590/S0100-204X2012000900004$2DOI
100 1 $aLU, D.
245 $aLand use/cover classification in the Brazilian Amazon using satellite images.
260 $c2012
300 $ap. 1185-1208.
520 $aLand use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation?based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi?resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical?based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.
650 $aTexture
650 $aTextura
653 $aClassificador não paramétrico
653 $aDado de sensor múltiplo
653 $aData fusion
653 $aFusão de dados
653 $aMultiple sensor data
653 $aNonparametric classifiers
700 1 $aBATISTELLA, M.
700 1 $aLI, G.
700 1 $aMORAN, E.
700 1 $aHETRICK, S.
700 1 $aFREITAS, C. DA C.
700 1 $aSANT'ANNA, S. J.
773 $tPesquisa Agropecuária Brasileira, Brasilia, DF$gv. 47, n. 9, p. 1185-1208, set. 2012.
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Registro original: |
Embrapa Territorial (CNPM) |
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Biblioteca |
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Origem |
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Registro |
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URL |
Voltar
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Hortaliças. Para informações adicionais entre em contato com cnph.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Hortaliças. |
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Data corrente: |
25/10/2002 |
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Data da última atualização: |
25/10/2002 |
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Autoria: |
VILLAS BOAS, G. L. |
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Afiliação: |
Embrapa Hortaliças, Brasília, DF. |
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Título: |
O perfil do inimigo: estudos da biologia da mosca-branca mostram que o inseto se adapta a diferentes condições climáticas e plantas hospedeiras. |
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Ano de publicação: |
2002 |
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Fonte/Imprenta: |
Cultivar Hortaliças e Frutas, Pelotas, v. 3, n. 16, p. 12-14, out./nov. 2002. |
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Idioma: |
Português |
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Conteúdo: |
Descrição do adulto; Ciclo biológico. |
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Palavras-Chave: |
Brasil; Evolução; Geminivirus; Pest; Whitefly. |
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Thesagro: |
Inseto; Mosca Branca; Planta Hospedeira; Praga. |
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Thesaurus NAL: |
evolution. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00752naa a2200241 a 4500
001 1770406
005 2002-10-25
008 2002 bl uuuu u00u1 u #d
100 1 $aVILLAS BOAS, G. L.
245 $aO perfil do inimigo$bestudos da biologia da mosca-branca mostram que o inseto se adapta a diferentes condições climáticas e plantas hospedeiras.
260 $c2002
520 $aDescrição do adulto; Ciclo biológico.
650 $aevolution
650 $aInseto
650 $aMosca Branca
650 $aPlanta Hospedeira
650 $aPraga
653 $aBrasil
653 $aEvolução
653 $aGeminivirus
653 $aPest
653 $aWhitefly
773 $tCultivar Hortaliças e Frutas, Pelotas$gv. 3, n. 16, p. 12-14, out./nov. 2002.
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