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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/475SM78
Repositóriosid.inpe.br/mtc-m21d/2022/06.22.12.37   (acesso restrito)
Última Atualização2022:06.22.12.37.34 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2022/06.22.12.37.34
Última Atualização dos Metadados2023:01.03.16.46.08 (UTC) administrator
DOI10.5194/isprs-archives-XLIII-B3-2022-721-2022
ISSN1682-1750
Chave de CitaçãoVieiraQueiShig:2022:AnInNu
TítuloAn analysis of the influence of the number of observations in a random forest time series classification to map the forest and deforestation in the brazilian Amazon
Ano2022
MêsJune
Data de Acesso16 abr. 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho7019 KiB
2. Contextualização
Autor1 Vieira, Leonardo de Souza
2 Queiroz, Gilberto Ribeiro de
3 Shiguemori, Elcio H.
Identificador de Curriculo1
2 8JMKD3MGP5W/3C9JHBC
Grupo1 CAP-COMP-DIPGR-INPE-MCTI-GOV-BR
2 DIOTG-CGCT-INPE-MCTI-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto de Estudos Avançados (IEAv)
Endereço de e-Mail do Autor1 leo76sv@gmail.com
2 gilberto.queiroz@inpe.br
3 elcio@ieav.cta.br
RevistaInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume43
NúmeroB3
Páginas721-728
Histórico (UTC)2022-06-22 12:38:01 :: simone -> administrator :: 2022
2022-07-08 16:51:17 :: administrator -> simone :: 2022
2022-12-20 14:14:10 :: simone -> administrator :: 2022
2023-01-03 16:46:08 :: 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-ChaveBrazilian Amazon
Classification
Land cover
Landsat
Random Forest
Time series
ResumoRemote sensing has been an essential tool in combating deforestation. However, the ever-rising deforestation rates require new remote sensing techniques. This paper presents a study to determine the effects on the accuracy of the data analysis of varying the number of satellite observations, using a Random Forest classification algorithm. We carried out experiments on the Landsat-8 data cube with 22 images and developed an automatic sampling system based on PRODES to generate the labeled time series. We split the time series dataset to build data subsets with different number of observations. The results showed that a fewer number of observations negatively effects the accuracy of the RF algorithm when analyzing deforested areas, but not forest areas. The RF classifiers were compared using a random test data set, where all classifiers presented an Overall Accuracy (OA), Balanced Accuracy (BA), and f1-score (F1) above 97%. In the first evaluation, the variation in the number of observations appears to cause little influence on the classification accuracy. The analysis used the reference map to contrast the RF classifier's results. The results showed that the best results in OA occurred with fewer observations. The best performance of 96% happened with four observations. We evaluated the performance of the classes, deforestation, and forest individually. The results showed that a fewer number of observations had negative effects on the accuracy of the RF algorithm when analyzing deforested areas, but not forest areas. Finally, we evaluated the visual quality of the land cover maps produced.
ÁreaCOMP
Arranjo 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > An analysis of...
Arranjo 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > An analysis of...
Arranjo 3urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > An analysis of...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 22/06/2022 09:37 1.0 KiB 
4. Condições de acesso e uso
Idiomaen
Arquivo Alvoisprs-archives-XLIII-B3-2022-721-2022.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3F2PHGS
8JMKD3MGPCW/46KUATE
8JMKD3MGPCW/46KUES5
Lista de Itens Citandosid.inpe.br/bibdigital/2022/04.03.23.11 2
sid.inpe.br/bibdigital/2013/10.12.22.16 1
DivulgaçãoWEBSCI; PORTALCAPES; COMPENDEX.
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
Campos Vaziosalternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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