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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W/43NH2FE
Repositorysid.inpe.br/plutao/2020/12.07.13.58.33
Last Update2020:12.08.21.13.59 (UTC) lattes
Metadata Repositorysid.inpe.br/plutao/2020/12.07.13.58.34
Metadata Last Update2022:01.04.01.31.18 (UTC) administrator
DOI10.14393/rbcv72n4-54044
ISSN0560-4613
1808-0936
Labellattes: 1175464822052393 2 MoreiraReKöDuCaAr:2020:SuAnMO
Citation KeyMoreiraReKöDuCaAr:2020:SuAnMO
TitleSubpixel analysis of MODIS imagery time series using transfer learning and relative calibration
Year2020
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PN
Number of Files1
Size1395 KiB
2. Context
Author1 Moreira, Noeli Aline Particcelli
2 Reis, Mariane Souza
3 Körting, Thales Sehn
4 Dutra, Luciano Vieira
5 Castejon, Emiliano Ferreira
6 Arai, Egídio
Resume Identifier1
2
3
4 8JMKD3MGP5W/3C9JHMA
5
6 8JMKD3MGP5W/3C9JGUP
ORCID1 0000-0002-5308-8080
2 0000-0001-9356-7652
3 0000-0002-0876-0501
4 0000-0002-7757-039X
5 0000-0002-4148-2830
6 0000-0003-1994-5277
Group1 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
2 CST-CST-SESPG-INPE-MCTIC-GOV-BR
3 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
4 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
5 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
6 DIDSR-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 noeli.aline@inpe.br
2 mariane.reis@inpe.br
3 thales.korting@inpe.br
4 luciano.dutra@inpe.br
5 emiliano.castejon@inpe.br
6 egidio.arai@inpe.br
JournalRevista Brasileira de Cartografia
Volume72
Number4
Pages558-573
Secondary MarkA2_INTERDISCIPLINAR A2_GEOGRAFIA A2_ARQUITETURA_E_URBANISMO B1_PLANEJAMENTO_URBANO_E_REGIONAL_/_DEMOGRAFIA B1_CIÊNCIAS_AMBIENTAIS B2_GEOCIÊNCIAS B3_ENGENHARIAS_I B4_ENGENHARIAS_III B4_CIÊNCIAS_SOCIAIS_APLICADAS_I B5_ENGENHARIAS_IV B5_ENGENHARIAS_II B5_CIÊNCIAS_AGRÁRIAS_I B5_BIODIVERSIDADE C_ZOOTECNIA_/_RECURSOS_PESQUEIROS C_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA C_CIÊNCIAS_BIOLÓGICAS_I C_ASTRONOMIA_/_FÍSICA
History (UTC)2020-12-07 15:16:48 :: lattes -> administrator :: 2020
2020-12-08 21:03:14 :: administrator -> lattes :: 2020
2020-12-08 21:14:00 :: lattes -> administrator :: 2020
2020-12-10 10:52:18 :: administrator -> lattes :: 2020
2020-12-14 14:08:48 :: lattes -> administrator :: 2020
2022-01-04 01:31:18 :: administrator -> simone :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsRelative Calibration.Image Time-series.Samples Extension.Subpixel Analysis. Land Cover classification
AbstractTransfer learning reuses a pre-trained model on a new related problem, which can be useful for monitoring large areas such as the Amazon biome. A given object must havesimilar spectral characteristics in the data usedfor this type of analysis, which can be achieved usingrelative calibration techniques. In this article, we present a relative calibration process in multitemporal images and evaluate its impacts on a subpixel classification process. MODIS images from the Amazon region, collected between 2013and 2017, were relatively calibrated using a 2012 image as reference and classified by transfer learning. Classifications of calibrated and uncalibrated images were compared with data from the PRODES project, focusing on forest areas. A great variation was observed in the spectral responses of the forest class, even in images of proximatedates and fromthe same sensor. These variations significantly impacted the land cover classifications in the subpixel, with cases of agreement between the uncalibrated data maps and PRODES of 0%. For calibrated data, the agreement values were greater than 70%. The results indicate that the method used, although quite simple, is adequate and necessary for the subpixel classification of MODIS images by transfer learning.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Subpixel analysis of...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Subpixel analysis of...
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doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP3W/43NH2FE
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W/43NH2FE
Languageen
Target Filemoreira_subpixel.pdf
Reader Groupadministrator
lattes
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/449U4PL
Citing Item Listsid.inpe.br/bibdigital/2013/09.09.15.05 1
sid.inpe.br/bibdigital/2013/09.13.21.11 1
URL (untrusted data)http://www.seer.ufu.br/index.php/revistabrasileiracartografia/article/view/54044
DisseminationPORTALCAPES
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
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