Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1084549
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dc.contributor.authorNEVES, M. C.
dc.contributor.authorNEVES JÚNIOR, O. R.
dc.contributor.authorLUIZ, A. J. B.
dc.contributor.authorSANCHES, I. D.
dc.date.accessioned2018-01-08T23:20:12Z-
dc.date.available2018-01-08T23:20:12Z-
dc.date.created2018-01-08
dc.date.issued2017
dc.identifier.citationIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais... Santos: Inpe, 2017. Trabalho 59957.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1084549-
dc.descriptionThe 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.
dc.language.isoporpt_BR
dc.rightsopenAccesspt_BR
dc.subjectImage processing
dc.subjectProcessamento de imagem
dc.subjectVisão computacional
dc.subjectÍndice de área foliar
dc.subjectImagem de satélite
dc.titleÍndice de cobertura verde para imagens de altíssima resolução.
dc.typeArtigo em anais e proceedings
dc.date.updated2018-03-12T11:11:11Zpt_BR
dc.subject.thesagroSensoriamento remoto
dc.subject.thesagroAgricultura
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusImage analysis
dc.subject.nalthesaurusComputer vision
dc.subject.nalthesaurusAgriculture
dc.subject.nalthesaurusLeaf area index
dc.format.extent2p. 1273-1280.
riaa.ainfo.id1084549
riaa.ainfo.lastupdate2018-03-12 -03:00:00
dc.contributor.institutionMARCOS CORREA NEVES, CNPMA; OTHON DA ROCHA NEVES JUNIOR, UFSC; ALFREDO JOSE BARRETO LUIZ, CNPMA; IEDA DEL'ARCO SANCHES, INPE.
Appears in Collections:Artigo em anais de congresso (CNPMA)

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