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| Registros recuperados : 267 | |
| Registros recuperados : 267 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
08/01/2018 |
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Data da última atualização: |
17/01/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
NEVES, M. C.; NEVES JÚNIOR, O. R.; LUIZ, A. J. B.; SANCHES, I. D. |
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Afiliação: |
MARCOS CORREA NEVES, CNPMA; OTHON DA ROCHA NEVES JUNIOR, UFSC; ALFREDO JOSE BARRETO LUIZ, CNPMA; IEDA DEL'ARCO SANCHES, INPE. |
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Título: |
Índice de cobertura verde para imagens de altíssima resolução. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais... Santos: Inpe, 2017. Trabalho 59957. |
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Páginas: |
p. 1273-1280. |
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Idioma: |
Português |
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Conteúdo: |
The low altitude aerial images are becoming more common every day due to low cost and ease of use of platforms such as remotely piloted aircraft. The potential application of this type of data is very high. One example is the precision agriculture, a farming management concept based on observing, measuring and responding to inter and intra-field variability in crops, an activity than can greatly benefit from this technology. The low altitude of image acquisition allows very high level of scene details but aggravates problems such as lighting variation and image deformation. In addition, often common cameras are used in different situations altitude, inclination, lighting and camera setup. These specific characteristics in relation to the orbital data require development of new methods and approaches to exploit the potential of data and to mitigate problems and limitations. In this work, we present a proposal for a method that provides a green coverage index. It reflects the green pixels density in an area. The proposed index has similar applicability to vegetation indices but does not require near-infrared data, not available in common cameras. We show problems especially related to agriculture applications, present initial test results discuss the possibilities and limitations of the method. |
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Palavras-Chave: |
Image processing; Imagem de satélite; Índice de área foliar; Processamento de imagem; Visão computacional. |
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Thesagro: |
Agricultura; Sensoriamento remoto. |
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Thesaurus NAL: |
Agriculture; Computer vision; Image analysis; Leaf area index; 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/170542/1/2017AA30.pdf
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Marc: |
LEADER 02257nam a2200301 a 4500
001 2084549
005 2023-01-17
008 2017 bl uuuu u00u1 u #d
100 1 $aNEVES, M. C.
245 $aÍndice de cobertura verde para imagens de altíssima resolução.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais... Santos: Inpe, 2017. Trabalho 59957.$c2017
300 $ap. 1273-1280.
520 $aThe low altitude aerial images are becoming more common every day due to low cost and ease of use of platforms such as remotely piloted aircraft. The potential application of this type of data is very high. One example is the precision agriculture, a farming management concept based on observing, measuring and responding to inter and intra-field variability in crops, an activity than can greatly benefit from this technology. The low altitude of image acquisition allows very high level of scene details but aggravates problems such as lighting variation and image deformation. In addition, often common cameras are used in different situations altitude, inclination, lighting and camera setup. These specific characteristics in relation to the orbital data require development of new methods and approaches to exploit the potential of data and to mitigate problems and limitations. In this work, we present a proposal for a method that provides a green coverage index. It reflects the green pixels density in an area. The proposed index has similar applicability to vegetation indices but does not require near-infrared data, not available in common cameras. We show problems especially related to agriculture applications, present initial test results discuss the possibilities and limitations of the method.
650 $aAgriculture
650 $aComputer vision
650 $aImage analysis
650 $aLeaf area index
650 $aRemote sensing
650 $aAgricultura
650 $aSensoriamento remoto
653 $aImage processing
653 $aImagem de satélite
653 $aÍndice de área foliar
653 $aProcessamento de imagem
653 $aVisão computacional
700 1 $aNEVES JÚNIOR, O. R.
700 1 $aLUIZ, A. J. B.
700 1 $aSANCHES, I. D.
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Embrapa Meio Ambiente (CNPMA) |
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