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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFRKFL
Repositorysid.inpe.br/sibgrapi/2017/08.21.23.09
Last Update2017:08.21.23.09.18 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.21.23.09.18
Metadata Last Update2022:06.14.00.09.00 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.41
Citation KeyBaetaNoguMenoSant:2017:LeDeFe
TitleLearning Deep Features on Multiple Scales for Coffee Crop Recognition
FormatOn-line
Year2017
Access Date2024, Apr. 28
Number of Files1
Size8809 KiB
2. Context
Author1 Baeta, Rafael
2 Nogueira, Keiller
3 Menotti, David
4 Santos, Jefersson Alex dos
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal do Paraná
4 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 Addressrbaeta@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-21 23:09:18 :: rbaeta@dcc.ufmg.br -> administrator ::
2022-06-14 00:09:00 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsDeep Learning
Remote Sensing
Coffee Crops
High-resolution Images
Agriculture
AbstractGeographic mapping of coffee crops by using remote sensing images and supervised classification has been a challenging research subject. Besides the intrinsic problems caused by the nature of multi-spectral information, coffee crops are non-seasonal and usually planted in mountains, which requires encoding and learning a huge diversity of patterns during the classifier training. In this paper, we propose a new approach for automatic mapping coffee crops by combining two recent trends on pattern recognition for remote sensing applications: deep learning and fusion/selection of features from multiple scales. The proposed approach is a pixel-wise strategy that consists in the training and combination of convolutional neural networks designed to receive as input different context windows around labeled pixels. Final maps are created by combining the output of those networks for a non-labeled set of pixels. Experimental results show that multiple scales produces better coffee crop maps than using single scales. Experiments also show the proposed approach is effective in comparison with baselines.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Learning Deep Features...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Learning Deep Features...
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source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFRKFL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFRKFL
Languageen
Target FilePID4960341.pdf
User Grouprbaeta@dcc.ufmg.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 5
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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