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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3J9RPF5
Repositorysid.inpe.br/mtc-m21b/2015/04.07.17.11   (restricted access)
Last Update2015:04.07.17.12.54 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21b/2015/04.07.17.11.47
Metadata Last Update2018:06.04.02.55.21 (UTC) administrator
DOI10.1109/TGRS.2014.2381645
ISSN0196-2892
Citation KeyZanottaBruzBovoShim:2015:AdSeAp
TitleAn adaptive semisupervised approach to the detection of user-defined recurrent changes in image time series
Year2015
MonthJul.
Access Date2024, Apr. 30
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size1722 KiB
2. Context
Author1 Zanotta, Daniel Capella
2 Bruzzone, Lorenzo
3 Bovolo, Francesca
4 Shimabukuro, Yosio Edemir
Resume Identifier1
2
3
4 8JMKD3MGP5W/3C9JJCQ
Group1
2
3
4 DSR-OBT-INPE-MCTI-GOV-BR
Affiliation1 National Institute for Science, Education and Technology, Rio Grande, Brazil
2 Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
3 Center for Information and Communication Technology, Fondazione Bruno Kessler, Trento, Italy
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1
2
3
4 yosio@ltid.inpe.br
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume53
Number7
Pages3707-3719
Secondary MarkA1_INTERDISCIPLINAR A1_GEOGRAFIA A1_GEOCIÊNCIAS A1_ENGENHARIAS_IV A1_CIÊNCIAS_AGRÁRIAS_I A1_CIÊNCIA_DA_COMPUTAÇÃO A2_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA
History (UTC)2015-04-07 17:11:47 :: simone -> administrator ::
2016-06-21 18:43:19 :: administrator -> simone :: 2015
2016-08-19 16:44:37 :: simone -> administrator :: 2015
2018-06-04 02:55:21 :: administrator -> simone :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsChange detection
deforestation
domain adaptation
forest fires
recurrent change
time series
AbstractIn this paper, we present a novel domain adaptation technique aimed at providing reliable change detection maps for a series of image pairs acquired on the same area at different times. The proposed technique exploits the polar change vector analysis method and assumes that the reference data for characterizing a specific change of interest are available only for a pair of images (source domain). Then, it exploits the knowledge learned from the source domain and adapts it to other pairs of images belonging to the time series (target domains) to be analyzed. The proposed technique is able to handle possible radiometric differences among images adapting in an unsupervised way the decision rule estimated on the source domain to the target domains through variables estimated directly on the target images. The proposed approach has been applied to two data sets made up of time series of Landsat Thematic Mapper images. In one case, the change of interest is related to evolution of deforestation, while in the other case, it is related to burned area detection. Experimental results show the effectiveness of the proposed technique.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > An adaptive semisupervised...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
Languageen
Target Filezanotta_adaptive.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher allowfinaldraft
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Next Higher Units8JMKD3MGPCW/3ER446E
DisseminationWEBSCI; PORTALCAPES; COMPENDEX; IEEEXplore; SCOPUS.
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
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