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Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyBaetaNoguMenoSant:2017:LeDeFe
TitleLearning Deep Features on Multiple Scales for Coffee Crop Recognition
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size8809 KiB
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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
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-21 23:09:18 :: -> administrator ::
2020-02-19 02:01:37 :: administrator -> :: 2017
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
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.
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