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Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2021: administrator
Citation KeyCastroFeiRosDiaSan:2017:CoAnDe
TitleA Comparative Analysis of Deep Learning Techniques for Sub-tropical Crop Types Recognition from Multitemporal Optical/SAR Image Sequences
Access Date2021, Mar. 08
Number of Files1
Size22342 KiB
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Author1 Castro, Jose Bermudez
2 Feitosa, Raul Queiroz
3 Rosa, Laura Cue La
4 Diaz, Pedro Achanccaray
5 Sanches, Ieda
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
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 :: -> administrator :: 2017
2021-02-23 03:51:52 :: administrator -> :: 2017
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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.
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