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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PK8MLE
Repositorysid.inpe.br/sibgrapi/2017/09.11.17.08
Last Update2017:09.11.17.08.18 (UTC) eteduardotavares@gmail.com
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.11.17.08.18
Metadata Last Update2022:05.18.22.18.26 (UTC) administrator
Citation KeyTavaresSant:2017:ExInSt
TitleExploiting indexing structures for large scale Remote Sensing Image Classification
FormatOn-line
Year2017
Access Date2024, Apr. 28
Number of Files1
Size341 KiB
2. Context
Author1 Tavares, Eduardo de Araújo
2 dos Santos, Jefersson Alex
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addresseteduardotavares@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeWork in Progress
History (UTC)2017-09-11 17:08:18 :: eteduardotavares@gmail.com -> administrator ::
2022-05-18 22:18:26 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsremote sensing
image indexing
AbstractThe rapid increase on the volume of data generated by remote sensing systems boosted by the evolution of satellites and the popularization of their imagery has enabled a wide range of new Earth Observation applications. At the same time, it created the challenge of how to efficiently deal with these collections of data. In this work we evaluate the use of indexing techniques for speeding up remote sensing image retrieval aiming automatic large scale geographical mapping in the future. Three CNNs are employed as feature extractors and compared to three low-level features on retrieval tasks performed on a dataset of aerial images with the LSH algorithm. Preliminary results showed a recall level of almost 50% when only roughly 5% of the samples of the evaluated dataset needed to be considered.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2017 > Exploiting indexing structures...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 11/09/2017 14:08 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PK8MLE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PK8MLE
Languageen
Target File2017_sibgrapi camera ready.pdf
User Groupeteduardotavares@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 9
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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