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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/4659LAE
Repositóriosid.inpe.br/mtc-m21d/2022/01.03.13.41
Última Atualização2022:01.03.13.41.05 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2022/01.03.13.41.05
Última Atualização dos Metadados2022:04.03.22.27.46 (UTC) administrator
DOI10.3390/rs13234944
ISSN2072-4292
Chave de CitaçãoKuckSiSaBiShSi:2021:ChDeSe
TítuloChange detection of selective logging in the brazilian amazon using x-band sar data and pre-trained convolutional neural networks
Ano2021
MêsDec.
Data de Acesso19 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho6873 KiB
2. Contextualização
Autor1 Kuck, Tahisa Neitzel
2 Silva Filho, Paulo Fernando Ferreira
3 Sano, Edson Eyji
4 Bispo, Popyanna da Conceição
5 Shiguemori, Elcio Hideiti
6 Silva, Ricardo Dal'Agnol da
ORCID1 0000-0003-0952-7055
2 0000-0003-0556-3470
3 0000-0001-5760-556X
4 0000-0003-0247-8449
5 0000-0001-5226-0435
6 0000-0002-7151-8697
Grupo1
2
3
4
5
6 DIOTG-CGCT-INPE-MCTI-GOV-BR
Afiliação1 Instituto de Estudos Avançados (IEAv)
2 Instituto de Estudos Avançados (IEAv)
3 Universidade de Brasília (UnB)
4 University of Manchester
5 Instituto de Estudos Avançados (IEAv)
6 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 thaisa@ieav.cta.br
2 silvafilho@ieav.cta.br
3 edson.sano@embrapa.br
4 polyanna.bispo@manchester.ac.uk
5 elcio@ieav.cta.br
6 ricds@hotmail.com
RevistaRemote Sensing
Volume13
Número23
Páginase4944
Nota SecundáriaB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
Histórico (UTC)2022-01-03 13:41:05 :: simone -> administrator ::
2022-01-03 13:41:07 :: administrator -> simone :: 2021
2022-01-03 13:42:46 :: simone -> administrator :: 2021
2022-04-03 22:27:46 :: administrator -> simone :: 2021
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-ChaveConvolutional neural networks
Selective logging
Synthetic aperture radar
ResumoIt is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques.
ÁreaSRE
Arranjourlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Change detection of...
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4. Condições de acesso e uso
URL dos dadoshttp://mtc-m21d.sid.inpe.br/ibi/8JMKD3MGP3W34T/4659LAE
URL dos dados zipadoshttp://mtc-m21d.sid.inpe.br/zip/8JMKD3MGP3W34T/4659LAE
Idiomaen
Arquivo Alvoremotesensing-13-04944-v2.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentoallowpublisher allowfinaldraft
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Repositório Espelhourlib.net/www/2021/06.04.03.40.25
Unidades Imediatamente Superiores8JMKD3MGPCW/46KUATE
Lista de Itens Citandosid.inpe.br/bibdigital/2022/04.03.22.23 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 nextedition notes 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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