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Reference TypeConference Proceedings
Identifier8JMKD3MGPDW34R/3UFE9MS
Repositorysid.inpe.br/mtc-m16d/2019/11.27.18.04
Metadatasid.inpe.br/mtc-m16d/2019/11.27.18.04.55
Sitemtc-m16d.sid.inpe.br
ISSN2179-4847
Citation KeyUeharaCoQuKöDuRe:2019:ClAlCo
Author1 Uehara, Tatiana Dias Tardelli
2 Correa, Sabrina Paes Leme Passos
3 Quevedo, Renata Pacheco
4 Körting, Thales Sehn
5 Dutra, Luciano Vieira
6 Rennó, Camilo Daleles
Group1 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
2 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
3 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
4 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
5 CGOBT-CGOBT-INPE-MCTIC-GOV-BR
6 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
6 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 tatiana.uehara@inpe.br
2 sabrina.correa@inpe.br
3 renata.quevedo@inpe.br
4 thales.korting@inpe.br
5 luciano.dutra@inpe.br
6 camilo.renno@inpe.br
TitleClassification algorithms comparison for landslide scars
Conference NameSimpósio Brasileiro de Geoinformática (GEOINFO)
Year2019
EditorLisboa Filho, Jugurta
Monteiro, Antonio Miguel Vieira
Book TitleAnais do 20º Simpósio Brasileiro de Geoinformática
Date11 -13 nov. 2019
Publisher CitySão José dos Campos
PublisherInstituto Nacional de Pesquisas Espaciais (INPE)
Conference LocationSão José dos Campos
Keywordsgeoinformatica.
AbstractLandslide inventory is an essential tool to support disaster risk mitigation. Using remote sensing images, it is usually obtained through pattern recognition. In this study, three classification methods are compared to detect landslides: Support Vector Machine (SVM), Artificial Neural Net (ANN) and Maximum Likelihood (ML). We used Sentinel-2A imagery, extracted and selected features for two areas in the Rolante River Catchment. The classification products showed that SVM classifier presented the best overall accuracy (OA) for Area 1 resulting in 87.143%; while for Area 2 ML showed the best OA equals to 86.831%.
OrganizationInstituto Nacional de Pesquisas Espaciais (INPE)
Languagept
Secondary TypePRE CN
Tertiary Typefull paper
FormatOn-line.
AreaSER
Size480 KiB
Number of Files1
Target File158-169.pdf
Last Update2019:11.27.18.04.55 sid.inpe.br/mtc-m19@80/2009/08.21.17.02 simone
Metadata Last Update2020:05.19.18.37.41 sid.inpe.br/mtc-m19@80/2009/08.21.17.02 simone {D 2019}
Document Stagecompleted
Is the master or a copy?is the master
Mirrorurlib.net/www/2011/03.29.20.55
e-Mail Addressdaniela.seki@inpe.br
e-Mail (login)simone
User Groupdaniela.seki@inpe.br
Visibilityshown
Transferable1
Host Collectionsid.inpe.br/mtc-m19@80/2009/08.21.17.02
Document Stagenot transferred
Copyright Repositoryurlib.net/www/2012/11.12.15.19
Rightsholderoriginalauthor yes
Read Permissionallow from all
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3EU2H28
source Directory Contentthere are no files
agreement Directory Contentthere are no files
History2019-11-27 18:04:55 :: daniela.seki@inpe.br -> administrator ::
2020-01-09 13:45:27 :: administrator -> simone :: 2019
2020-01-09 13:52:37 :: simone -> administrator :: 2019
2020-05-19 15:00:30 :: administrator -> simone :: 2019
Empty Fieldsaccessionnumber archivingpolicy archivist callnumber contenttype copyholder creatorhistory descriptionlevel dissemination doi edition holdercode isbn label lineage mark nextedition notes numberofvolumes orcid pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup resumeid secondarydate secondarykey secondarymark serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
Access Date2020, May 25
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