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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3UMECLL
Repositorysid.inpe.br/mtc-m21c/2020/01.03.16.24   (restricted access)
Last Update2020:01.03.16.24.37 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2020/01.03.16.24.37
Metadata Last Update2022:01.04.01.34.55 (UTC) administrator
DOI10.1007/978-3-030-19642-4_7
ISSN2194-5357
Citation KeySantosFerrPicoCâma:2020:SeMaEa
TitleSelf-organizing maps in Earth observation data cubes analysis
Year2020
Access Date2024, May 18
Type of Workconference paper
Secondary TypePRE PI
Number of Files1
Size1633 KiB
2. Context
Author1 Santos, Lorena Alves dos
2 Ferreira, Karine Reis
3 Picoli, Michelle Cristina Araújo
4 Câmara, Gilberto
Resume Identifier1
2 8JMKD3MGP5W/3C9JHKN
3
4 8JMKD3MGP5W/3C9JHB8
Group1 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
2 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
3 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
4 DIDPI-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)
Author e-Mail Address1 lorena.santos@inpe.br
2 karine.ferreira@inpe.br
3 mipicoli@gmail.com
4 gilberto.camara@inpe.br
JournalAdvances in Intelligent Systems and Computing
Volume976
Pages70-79
History (UTC)2020-01-03 16:25:32 :: simone :: 2019 -> 2020
2020-01-03 16:25:33 :: simone -> administrator :: 2020
2020-01-05 12:26:59 :: administrator -> simone :: 2020
2020-06-23 22:52:15 :: simone -> administrator :: 2020
2022-01-04 01:34:55 :: administrator -> simone :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
AbstractEarth Observation (EO) Data Cubes infrastructures model analysis-ready data generated from remote sensing images as multidimensional cubes (space, time and properties), especially for satellite image time series analysis. These infrastructures take advantage of big data technologies and methods to store, process and analyze the big amount of Earth observation satellite images freely available nowadays. Recently, EO Data Cubes infrastructures and satellite image time series analysis have brought new opportunities and challenges for the Land Use and Cover Change (LUCC) monitoring over large areas. LUCC have caused a great impact on tropical ecosystems, increasing global greenhouse gases emissions and reducing the planets biodiversity. This paper presents the utility of Self-Organizing Maps (SOM) neural network method in the process to extract LUCC information from EO Data Cubes infrastructures, using image time series analysis. Most classification techniques to create LUCC maps from satellite image time series are based on supervised learning methods. In this context, SOM is used as a method to assess land use and cover samples and to evaluate which spectral bands and vegetation indexes are best suitable for the separability of land use and cover classes. A case study is described in this work and shows the potential of SOM in this application.
AreaSRE
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4. Conditions of access and use
Languageen
Target Filesantos_self.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3F2PHGS
Citing Item Listsid.inpe.br/bibdigital/2013/09.09.15.05 4
sid.inpe.br/mtc-m21/2012/07.13.14.53.08 1
sid.inpe.br/bibdigital/2013/10.12.22.16 1
DisseminationWEBSCI; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
Notes13th International Workshop on Self-Organizing Maps, WSOM+ 2019; Barcelona; Spain; 26-28 June 2019.
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