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Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m12.sid.inpe.br
Identifier8JMKD3MGPAW/3PFRT45
Repositorysid.inpe.br/sibgrapi/2017/08.22.00.41
Last Update2017:08.22.13.58.53 administrator
Metadatasid.inpe.br/sibgrapi/2017/08.22.00.41.47
Metadata Last Update2021:02.23.03.51.52 administrator
Citation KeyCastroFeiRosDiaSan:2017:CoAnDe
TitleA Comparative Analysis of Deep Learning Techniques for Sub-tropical Crop Types Recognition from Multitemporal Optical/SAR Image Sequences
FormatOn-line
Year2017
Access Date2021, Mar. 08
Number of Files1
Size22342 KiB
Context area
Author1 Castro, Jose Bermudez
2 Feitosa, Raul Queiroz
3 Rosa, Laura Cue La
4 Diaz, Pedro Achanccaray
5 Sanches, Ieda
Group1
2
3
4
5 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Affiliation1 Pontifical Catholic University of Rio de Janeiro
2 Pontifical Catholic University of Rio de Janeiro
3 Pontifical Catholic University of Rio de Janeiro
4 Pontifical Catholic University of Rio de Janeiro
5 National Institute for Space Research
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 Addressbermudez@ele.puc-rio.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-22 13:58:53 :: bermudez@ele.puc-rio.br -> administrator :: 2017
2021-02-23 03:51:52 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is a copy
Content Stagecompleted
Content TypeExternal Contribution
KeywordsCrop Recognition, Multitemporal Images, Autoencoders, Convolutional Neural Networks.
AbstractRemote Sensing (RS) data have been increasingly applied to assess agricultural yield, production and crop condition. In tropical areas, crop dynamics are complex due to multiple agricultural practices such as irrigation, non-tillage, crop rotation and multiple harvest per year. Spatial and temporal information can improve the performance in land-cover and crop type classification tasks. In this context Deep Learning (DL) have emerged as a powerful state-of-the-art technique in the RS community. This work presents a comparative analysis of traditional and DL (supervised and unsupervised) approaches for crop classification on sequences of multitemporal optical and SAR images. Three different approaches are compared: the image stacking approach, which is used as baseline, and two DL based approaches using Autoencoders (AEs) and Convolutional Neural Networks (CNNs). Experiments were carried out in two datasets from two different municipalities in Brazil, Ipu~{a} in S~{a}o Paulo state and Campo Verde in Mato Grosso state. It is shown that CNN and AE outperformed the traditional approach based on image stacking in terms of Overall Accuracy and Class Accuracy.
Arrangement 1SIBGRAPI 2017 > A Comparative Analysis...
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data URLhttp://urlib.net/rep/8JMKD3MGPAW/3PFRT45
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFRT45
Languageen
Target File2017_SIBGRAPI_BERMUDEZ.pdf
User Groupbermudez@ele.puc-rio.br
Visibilityshown
Update Permissionnot transferred
Allied materials area
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPCW/3ER446E
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Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume

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