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1. Identity statement
Reference TypeBook Section
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3R3EGP2
Repositorysid.inpe.br/mtc-m21c/2018/05.04.18.36   (restricted access)
Last Update2018:05.04.18.36.03 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2018/05.04.18.36.03
Metadata Last Update2020:12.07.21.11.36 (UTC) administrator
DOI10.1007/978-3-319-77028-4_43
ISSN2194-5357
Citation KeySilveiraJrCecaSantRibe:2018:ThSpAs
TitleThematic spatiotemporal association rules to track the evolving of visual features and their meaning in satellite image time series
Year2018
Access Date2024, May 19
Secondary TypePRE LI
Number of Files1
Size718 KiB
2. Context
Author1 Silveira Junior, Carlos R.
2 Cecatto, José Roberto
3 Santos, Matilde T. P.
4 Ribeiro, Marcela X.
Resume Identifier1
2 8JMKD3MGP5W/3C9JHJB
Group1
2 DIDAS-CGCEA-INPE-MCTIC-GOV-BR
Affiliation1 Universidade Federal de São Carlos (UFSCar)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Universidade Federal de São Carlos (UFSCar)
4 Universidade Federal de São Carlos (UFSCar)
Author e-Mail Address1 carlos.silveira@dc.ufscar.br
2 jr.cecatto@inpe.br
3 marilde@dc.ufscar.br
4 marcela@dc.ufscar.br
EditorLafiti, S.
Book TitleAdvances in intelligent systems and computing
PublisherSpringer
Volume738
Pages317-323
History (UTC)2018-05-04 18:36:03 :: simone -> administrator ::
2018-05-04 18:36:33 :: administrator -> simone :: 2018
2018-05-04 18:37:43 :: simone -> administrator :: 2018
2018-07-04 17:51:14 :: administrator -> simone :: 2018
2018-07-06 12:22:05 :: simone -> administrator :: 2018
2020-12-07 21:11:36 :: administrator -> simone :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsImage classification · Spatiotemporal association classifier · Solar data · Thematic spatiotemporal association rules extraction · Temporal series of images · Temporal series of semantic data
AbstractSatellite Image Time Series (SITS) is a set of images taken from the same satellite scene at different times. The mining of SITS is challenging task because it requires spatiotemporal data analysis. An example of the need for SITS mining is the analysis of solar flares and their evolving. Thematic Spatiotemporal Association Rules (TSARs) are associations that show spatiotemporal relationships among the values of the thematics attributes. By employing TSARs, we propose an approach to track the evolving of visual features of SITS images and their meaning. Our approach, called Miner of Thematic Spatiotemporal Associations for Images (MiTSAI), considers the data extracting and transformation, the thematic spatiotemporal association rule mining (TSARs), and the post-processing of the mined TSARs, that relate the visual features and their meaning. Our experiment shows that the proposed approach improves the domain expert team understanding of Solar SITS. Moreover, MiTSAI presented an acceptable time performance being able of extracting and processing TSARs using a long period of historical data faster than the period needed for the arrival of new data in the database.
AreaCEA
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDAS > Thematic spatiotemporal association...
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4. Conditions of access and use
Languageen
User Groupself-uploading-INPE-MCTI-GOV-BR
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Reader Groupadministrator
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Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ETR8EH
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.52.30 3
sid.inpe.br/bibdigital/2013/10.01.00.07 2
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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