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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34T/47DGTQH
Repositóriosid.inpe.br/mtc-m21d/2022/08.08.12.16
Última Atualização2022:08.08.12.16.07 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2022/08.08.12.16.07
Última Atualização dos Metadados2023:01.03.16.46.11 (UTC) administrator
DOI10.3390/rs14143290
ISSN2072-4292
Chave de CitaçãoAdarmePrieFeitAlme:2022:ImDeDe
TítuloImproving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks
Ano2022
Data de Acesso13 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho12785 KiB
2. Contextualização
Autor1 Adarme, Mabel Ortega
2 Prieto, Juan Doblas
3 Feitosa, Raul Queiroz
4 Almeida, Claudio Aparecido de
ORCID1 0000-0002-4106-0291
2 0000-0002-2573-3783
3 0000-0001-8344-5096
4 0000-0002-1032-6966
Grupo1
2 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
3
4 DIPE1-COGPI-INPE-MCTI-GOV-BR
Afiliação1 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 mortega@aluno.puc-rio.br
2 juan.doblas@inpe.br
3 raul@ele.puc-rio.br
4 claudio.almeida@inpe.br
RevistaRemote Sensing
Volume14
Páginase3290
Nota SecundáriaB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
Histórico (UTC)2022-08-08 12:16:07 :: simone -> administrator ::
2022-08-08 12:16:07 :: administrator -> simone :: 2022
2022-08-08 12:16:47 :: simone -> administrator :: 2022
2022-08-29 18:41:25 :: administrator -> simone :: 2022
2022-12-19 18:51:57 :: simone -> administrator :: 2022
2023-01-03 16:46:11 :: administrator -> simone :: 2022
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-Chavedeep learning
deforestation detection
stabilization
synthetic aperture radar
time series

tropical rainforest
ResumoDetecting early deforestation is a fundamental process in reducing forest degradation and carbon emissions. With this procedure, it is possible to monitor and control illegal activities associated with deforestation. Most regular monitoring projects have been recently proposed, but most of them rely on optical imagery. In addition, these data are seriously restricted by cloud coverage, especially in tropical environments. In this regard, Synthetic Aperture Radar (SAR) is an attractive alternative that can fill this observational gap. This work evaluated and compared a conventional method based on time series and a Fully Convolutional Network (FCN) with bi-temporal SAR images. These approaches were assessed in two regions of the Brazilian Amazon to detect deforestation between 2019 and 2020. Different pre-processing techniques, including filtering and stabilization stages, were applied to the C-band Sentinel-1 images. Furthermore, this study proposes to provide the network with the distance map to past-deforestation as additional information to the pair of images being compared. In our experiments, this proposal brought up to 4% improvement in average precision. The experimental results further indicated a clear superiority of the DL approach over a time series-based deforestation detection method used as a baseline in all experiments. Finally, the study proved the benefits of pre-processing techniques when using detection methods based on time series. On the contrary, the analysis revealed that the neural network could eliminate noise from the input images, making filtering innocuous and, therefore, unnecessary. On the other hand, the stabilization of the input images brought non-negligible accuracy gains to the DL approach.
ÁreaSRE
Arranjo 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Improving Deforestation Detection...
Arranjo 2urlib.net > Fonds > Produção a partir de 2021 > CGCT > Improving Deforestation Detection...
Arranjo 3urlib.net > BDMCI > Fonds > Produção a partir de 2021 > COGPI > Improving Deforestation Detection...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 08/08/2022 09:16 1.0 KiB 
4. Condições de acesso e uso
URL dos dadoshttp://mtc-m21d.sid.inpe.br/ibi/8JMKD3MGP3W34T/47DGTQH
URL dos dados zipadoshttp://mtc-m21d.sid.inpe.br/zip/8JMKD3MGP3W34T/47DGTQH
Idiomaen
Arquivo Alvoremotesensing-14-03290.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentoallowpublisher allowfinaldraft
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/46KUATE
8JMKD3MGPCW/46L2FGP
Lista de Itens Citandosid.inpe.br/bibdigital/2022/04.03.22.23 3
sid.inpe.br/bibdigital/2013/10.18.22.34 2
sid.inpe.br/bibdigital/2022/04.04.04.47 1
DivulgaçãoWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository month nextedition notes number parameterlist parentrepositories previousedition previouslowerunit progress project readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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