Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/LGRS.2019.2903194 http://hdl.handle.net/11449/197504 |
Resumo: | Plant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plant monitoring, demanding the creation of methods capable of locating, and identifying plant species through time and space. However, this is a challenging task given the high volume of data, the constant data missing from temporal dataset, the heterogeneity of temporal profiles, the variety of plant visual patterns, and the unclear definition of individuals' boundaries in plant communities. In this letter, we propose a novel method, suitable for phenological monitoring, based on convolutional networks (ConvNets) to perform spatio-temporal vegetation pixel classification on high-resolution images. We conducted a systematic evaluation using high-resolution vegetation image datasets associated with the Brazilian Cerrado biome. Experimental results show that the proposed approach is effective, overcoming other spatio-temporal pixel-classification strategies. |
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Spatio-Temporal Vegetation Pixel Classification by Using Convolutional NetworksDeep learningnear surfacephenologypixel classificationunmanned aerial vehiclesPlant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plant monitoring, demanding the creation of methods capable of locating, and identifying plant species through time and space. However, this is a challenging task given the high volume of data, the constant data missing from temporal dataset, the heterogeneity of temporal profiles, the variety of plant visual patterns, and the unclear definition of individuals' boundaries in plant communities. In this letter, we propose a novel method, suitable for phenological monitoring, based on convolutional networks (ConvNets) to perform spatio-temporal vegetation pixel classification on high-resolution images. We conducted a systematic evaluation using high-resolution vegetation image datasets associated with the Brazilian Cerrado biome. Experimental results show that the proposed approach is effective, overcoming other spatio-temporal pixel-classification strategies.Pro-Reitoria de Pesquisa da UFMGFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Cedro Textil, Reserva Vellozia, Parque Nacional da Serra do CipoUniv Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG, BrazilUniv Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP, BrazilUniv Estadual Paulista, Inst Biociencias, BR-13506900 Sao Paulo, BrazilUniv Estadual Paulista, IGCE, BR-13506900 Sao Paulo, BrazilUniv Stirling, Biol & Environm Sci, Fac Nat Sci, Stirling FK9 4LA, ScotlandUniv Estadual Paulista, Inst Biociencias, BR-13506900 Sao Paulo, BrazilUniv Estadual Paulista, IGCE, BR-13506900 Sao Paulo, BrazilFAPEMIG: APQ-00449-17FAPESP: 2013/50155-0FAPESP: 2013/50169-1FAPESP: 2009/54208-6FAPESP: 2016/26170-8FAPESP: 2018/06918-3CNPq: 424700/2018-2CAPES: 001CAPES: 88881.145912/2017-01Cedro Textil, Reserva Vellozia, Parque Nacional da Serra do Cipo: PELD-CRSC-17Ieee-inst Electrical Electronics Engineers IncUniversidade Federal de Minas Gerais (UFMG)Universidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (Unesp)Univ StirlingNogueira, KeillerSantos, Jefersson A. dosMenini, NathaliaSilva, Thiago S. F. [UNESP]Morellato, Leonor Patricia C. [UNESP]Torres, Ricardo da S.2020-12-11T00:48:55Z2020-12-11T00:48:55Z2019-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1665-1669http://dx.doi.org/10.1109/LGRS.2019.2903194Ieee Geoscience And Remote Sensing Letters. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 16, n. 10, p. 1665-1669, 2019.1545-598Xhttp://hdl.handle.net/11449/19750410.1109/LGRS.2019.2903194WOS:000489756100032Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Geoscience And Remote Sensing Lettersinfo:eu-repo/semantics/openAccess2021-10-22T21:03:13Zoai:repositorio.unesp.br:11449/197504Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:10:44.024920Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks |
title |
Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks |
spellingShingle |
Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks Nogueira, Keiller Deep learning near surface phenology pixel classification unmanned aerial vehicles |
title_short |
Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks |
title_full |
Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks |
title_fullStr |
Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks |
title_full_unstemmed |
Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks |
title_sort |
Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks |
author |
Nogueira, Keiller |
author_facet |
Nogueira, Keiller Santos, Jefersson A. dos Menini, Nathalia Silva, Thiago S. F. [UNESP] Morellato, Leonor Patricia C. [UNESP] Torres, Ricardo da S. |
author_role |
author |
author2 |
Santos, Jefersson A. dos Menini, Nathalia Silva, Thiago S. F. [UNESP] Morellato, Leonor Patricia C. [UNESP] Torres, Ricardo da S. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Minas Gerais (UFMG) Universidade Estadual de Campinas (UNICAMP) Universidade Estadual Paulista (Unesp) Univ Stirling |
dc.contributor.author.fl_str_mv |
Nogueira, Keiller Santos, Jefersson A. dos Menini, Nathalia Silva, Thiago S. F. [UNESP] Morellato, Leonor Patricia C. [UNESP] Torres, Ricardo da S. |
dc.subject.por.fl_str_mv |
Deep learning near surface phenology pixel classification unmanned aerial vehicles |
topic |
Deep learning near surface phenology pixel classification unmanned aerial vehicles |
description |
Plant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plant monitoring, demanding the creation of methods capable of locating, and identifying plant species through time and space. However, this is a challenging task given the high volume of data, the constant data missing from temporal dataset, the heterogeneity of temporal profiles, the variety of plant visual patterns, and the unclear definition of individuals' boundaries in plant communities. In this letter, we propose a novel method, suitable for phenological monitoring, based on convolutional networks (ConvNets) to perform spatio-temporal vegetation pixel classification on high-resolution images. We conducted a systematic evaluation using high-resolution vegetation image datasets associated with the Brazilian Cerrado biome. Experimental results show that the proposed approach is effective, overcoming other spatio-temporal pixel-classification strategies. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-01 2020-12-11T00:48:55Z 2020-12-11T00:48:55Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/LGRS.2019.2903194 Ieee Geoscience And Remote Sensing Letters. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 16, n. 10, p. 1665-1669, 2019. 1545-598X http://hdl.handle.net/11449/197504 10.1109/LGRS.2019.2903194 WOS:000489756100032 |
url |
http://dx.doi.org/10.1109/LGRS.2019.2903194 http://hdl.handle.net/11449/197504 |
identifier_str_mv |
Ieee Geoscience And Remote Sensing Letters. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 16, n. 10, p. 1665-1669, 2019. 1545-598X 10.1109/LGRS.2019.2903194 WOS:000489756100032 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ieee Geoscience And Remote Sensing Letters |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1665-1669 |
dc.publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129294194442240 |