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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RP9FF2
Repositorysid.inpe.br/sibgrapi/2018/09.03.15.27
Last Update2018:09.03.15.27.41 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/09.03.15.27.41
Metadata Last Update2022:06.14.00.09.20 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00013
Citation KeyDallaquaFariFaze:2018:AcLeAp
TitleActive Learning Approaches for Deforested Area Classification
FormatOn-line
Year2018
Access Date2024, May 02
Number of Files1
Size1003 KiB
2. Context
Author1 Dallaqua, Fernanda B. J. R.
2 Faria, Fabio A.
3 Fazenda, Alvaro L.
Affiliation1 UNIFESP
2 UNIFESP
3 UNIFESP
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressfernandab.dallaqua@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-09-03 15:27:41 :: fernandab.dallaqua@gmail.com -> administrator ::
2022-06-14 00:09:20 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsForest Monitoring
Active Learning
Remote Sensing Imagery
AbstractThe conservation of tropical forests is a social and ecological relevant subject because of its important role in the global ecosystem. Forest monitoring is mostly done by extraction and analysis of remote sensing imagery (RSI) information. In the literature many works have been successful in remote sensing image classification through the use of machine learning techniques. Generally, traditional learning algorithms demand a representative and huge training set which can be an expensive procedure, especially in RSI, where the imagery spectrum varies along seasons and forest coverage. A semi-supervised learning paradigm known as active learning (AL) is proposed to solve this problem, as it builds efficient training sets through iterative improvement of the model performance. In the construction process of training sets, unlabeled samples are evaluated by a user-defined heuristic, ranked and then the most relevant samples are labeled by an expert user. In this work two different AL approaches (Confidence Heuristics and Committee) are presented to classify remote sensing imagery. In the experiments, our AL approaches achieve excellent effectiveness results compared with well-known approaches existing in the literature for two different datasets.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2018 > Active Learning Approaches...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Active Learning Approaches...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RP9FF2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RP9FF2
Languageen
Target FilesibgrapiID116.pdf
User Groupfernandab.dallaqua@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 7
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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 volume


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