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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemarte2.sid.inpe.br
Identifier8JMKD3MGP6W34M/3U67ADP
Repositorysid.inpe.br/marte2/2019/10.01.19.20
Last Update2019:10.01.19.20.15 (UTC) administrator
Metadata Repositorysid.inpe.br/marte2/2019/10.01.19.20.15
Metadata Last Update2019:12.13.01.05.31 (UTC) administrator
ISBN978-85-17-00097-3
Citation KeyAdarmeHappFeit:2019:AsEaFu
TitleAssessment of an early fusion CNN approach applied to the deforestation detection in the Brazilian Amazon
FormatInternet
Year2019
Access Date2024, May 05
Secondary TypePRE CN
Number of Files1
Size889 KiB
2. Context
Author1 Adarme, Mabel Ortega
2 Happ, Patrick Nigri
3 Feitosa, Raul Queiroz
Affiliation1 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
2 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
3 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
Author e-Mail Address1 mortega@ele.puc-rio.br
2 patrick@ele.puc-rio.br
3 raul@ele.puc-rio.br
EditorGherardi, Douglas Francisco Marcolino
Sanches, Ieda DelArco
Aragão, Luiz Eduardo Oliveira e Cruz de
Conference NameSimpósio Brasileiro de Sensoriamento Remoto, 19 (SBSR)
Conference LocationSantos
Date14-17 abril 2019
PublisherInstituto Nacional de Pesquisas Espaciais (INPE)
Publisher CitySão José dos Campos
Pages1217-1220
Book TitleAnais
Tertiary Typefull paper
OrganizationInstituto Nacional de Pesquisas Espaciais (INPE)
History (UTC)2019-11-01 12:07:20 :: simone -> administrator :: 2019
2019-12-13 01:05:31 :: administrator -> simone :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsDeep learning
deforestation
image classification
early fusion
image stacking
AbstractDeforestation is one of the main causes of biodiversity reduction, climate change among others destructive phenomena. Thus, early detection of deforestation processes is of paramount importance in the recent year. Motivated by this scenario the present work focuses on assessing a DL approach called Early Fusion (EF) for automatic deforestation detection. Change detection approaches based on Random Forest (RF) and Change Vector Analysis (CVA) were adopted as baselines for comparison purposes. These approaches were evaluated in a region located in the state of Pará, Brazil, where two images from Landsat 8 satellite were acquired to detect deforested areas from 2016 to 2017. Their corresponding references were collected from the Satellite Deforestation Monitoring Project in the Legal Amazon (PRODES). In the experiments, the EF approach outperformed RF and CVA baselines, identifying in a better way the regions that have suffered deforestation.
AreaSRE
TypeMudança de uso e cobertura da Terra
Arrangement 1urlib.net > BDMCI > Fonds > SBSR > SBSR 19 > Assessment of an...
Arrangement 2urlib.net > DIDSR > SBSR 19 > Assessment of an...
Arrangement 3Projeto Memória 60... > Livros e livros editados > SBSR 19 > Assessment of an...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP6W34M/3U67ADP
zipped data URLhttp://urlib.net/zip/8JMKD3MGP6W34M/3U67ADP
Languagept
Target File97707.pdf
User Groupsimone
Visibilityshown
Copyright Licenseurlib.net/www/2012/11.12.15.19
Rightsholderoriginalauthor yes
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Next Higher Units8JMKD3MGP6W34M/3UCAT7H
Citing Item Listsid.inpe.br/marte2/2019/11.08.12.52 1
Host Collectiondpi.inpe.br/marte2/2013/05.17.15.03.06
6. Notes
Empty Fieldsarchivingpolicy archivist callnumber contenttype copyholder creatorhistory descriptionlevel dissemination doi e-mailaddress edition group holdercode issn label lineage mark nextedition notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid schedulinginformation secondarydate secondarykey secondarymark serieseditor session shorttitle sponsor subject tertiarymark url versiontype volume
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