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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/477R9JL
Repositorysid.inpe.br/mtc-m21d/2022/07.04.12.03   (restricted access)
Last Update2022:07.04.12.03.29 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2022/07.04.12.03.29
Metadata Last Update2023:01.03.16.46.09 (UTC) administrator
DOI10.1080/10106049.2020.1773544
ISSN1010-6049
Citation KeyDinizGamaAdam:2022:EvPoIn
TitleEvaluation of polarimetry and interferometry of sentinel-1A SAR data for land use and land cover of the Brazilian Amazon Region
Year2022
Access Date2024, May 19
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size2279 KiB
2. Context
Author1 Diniz, Juliana Maria Ferreira de Souza
2 Gama, Fábio Furlan
3 Adami, Marcos
Resume Identifier1
2 8JMKD3MGP5W/3C9JH3P
Group1 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
2 DIOTG-CGCT-INPE-MCTI-GOV-BR
3 DIOTG-CGCT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 juliana.diniz@inpe.br
2 fabio.furlan@inpe.br
3 adami16@gmail.com
JournalGeocarto International
Volume37
Number5
Pages1482-1500
Secondary MarkA2_GEOGRAFIA B1_INTERDISCIPLINAR B2_CIÊNCIAS_AMBIENTAIS
History (UTC)2022-07-04 12:03:29 :: simone -> administrator ::
2022-07-04 12:03:32 :: administrator -> simone :: 2022
2022-07-04 12:03:45 :: simone -> administrator :: 2022
2023-01-03 16:46:09 :: administrator -> simone :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsDual-polarimetric
interferometric coherence
land cover mapping
machine learning algorithms
AbstractSynthetic aperture radar (SAR) data has been an alternative for monitoring ground targets, especially in areas with cloud cover. This study evaluates the potential of Sentinel-1A attributes for mapping land use and land cover (LULC) in a region of the Brazilian Amazon, using two different machine learning classifiers: Random Forest (RF) and Support Vector Machine (SVM). Different scenarios were used that combined backscattering, polarimetry, and interferometry to the classification process, which was divided into two phases to improve the results. The RF shows superiority over the SVM for almost all scenarios for the two phases of the mapping. The scenario with all data, presented the best results with both classifiers. The final maps with RF and SVM, obtained a global accuracy of 82.7% and 74.5%, respectively. This study demonstrated the potential of Sentinel-1 to map LULC classes in the Amazon region using a classification in two phases.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Evaluation of polarimetry...
Arrangement 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Evaluation of polarimetry...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
Languageen
Target File10.1080@10106049.2020.1773544.pdf
User Groupsimone
Reader Groupadministrator
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Visibilityshown
Archiving Policydenypublisher denyfinaldraft12
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/46KUATE
Citing Item Listsid.inpe.br/bibdigital/2013/10.18.22.34 4
sid.inpe.br/bibdigital/2022/04.03.22.23 2
sid.inpe.br/mtc-m21/2012/07.13.14.45.57 1
DisseminationWEBSCI
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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