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1. Identity statement
Reference TypeBook Section
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3PF78MB
Repositorysid.inpe.br/mtc-m21b/2017/08.17.18.33   (restricted access)
Last Update2017:09.29.19.29.17 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21b/2017/08.17.18.33.40
Metadata Last Update2018:06.04.02.27.44 (UTC) administrator
Secondary KeyINPE--/
ISBN978-331962406-8
Citation KeySantosSiFeQuCaSa:2017:ClMeAs
TitleClustering methods to asses land cover samples of modis vegetation indexes time series
Year2017
Access Date2024, Apr. 26
Secondary TypePRE LI
Number of Files1
Size3865 KiB
2. Context
Author1 Santos, Lorena Alves dos
2 Simões, Rolf Ezequiel de Oliveira
3 Ferreira, Karine Reis
4 Queiroz, Gilberto Ribeiro
5 Camara, Gilberto
6 Santos, Rafael Duarte Coelho dos
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JHKN
4
5 8JMKD3MGP5W/3C9JHB8
6 8JMKD3MGP5W/3C9JJ4N
Group1 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
2 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
3 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
4 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
5 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
6 LABAC-COCTE-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 lorena.santos@inpe.br
2 rolf.simoes@inpe.br
3 karine.ferreira@inpe.br
4 gilberto.queiroz@inpe.br
5 gilberto.camara@inpe.br
6 rafael.santos@inpe.br
EditorGervasi, Osvaldo
Murgante, Beniamino
Misra, Sanjay
Borruso, Giuseppe
Torre, Carmelo M.
Rocha, Ana Maria A. C.
Taniar, David
Apduhan, Bernady O.
Stankova, Elena
Cuzzocrea, Alfredo
Book TitleComputational Science and Its Applications – ICCSA 2017
PublisherSpringer
Pages662-673
Series TitleLecture Notes in Computer Science , 10409
History (UTC)2017-08-17 18:34:00 :: simone -> administrator :: 2017
2017-08-24 09:30:40 :: administrator -> simone :: 2017
2017-12-14 18:00:50 :: simone -> administrator :: 2017
2018-01-09 11:46:33 :: administrator -> simone :: 2017
2018-01-09 18:21:54 :: simone -> administrator :: 2017
2018-06-04 02:27:44 :: administrator -> simone :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsTime series clustering
MODIS vegetation indexes
Land cover change classification
Self-Organizing Map (SOM)
AbstractMODIS vegetation indexes time series have been widely used to build land cover change maps on large scales. In this scope, to obtain good quality maps using supervised classification methods, it is crucial to select representative training samples of land cover change classes. In this paper, we evaluate two clustering methods, Hierarchical and Self-Organizing Map (SOM), to assess land cover samples of MODIS vegetation indexes time series. As we show, these techniques are suitable tools for assisting users to select representative land cover change samples from MODIS vegetation indexes time series. We present the accuracy of both methods for a case study in Ipiranga do Norte municipality in Mato Grosso state, Brazil.
AreaCOMP
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Clustering methods to...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Clustering methods to...
Arrangement 3urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > Clustering methods to...
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4. Conditions of access and use
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User Groupsimone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ESGTTP
8JMKD3MGPCW/3F2PHGS
DisseminationBNDEPOSITOLEGAL
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
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