@InProceedings{BaetaNoguMenoSant:2017:LeDeFe,
author = "Baeta, Rafael and Nogueira, Keiller and Menotti, David and Santos,
Jefersson Alex dos",
affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal
de Minas Gerais} and {Universidade Federal do Paran{\'a}} and
{Universidade Federal de Minas Gerais}",
title = "Learning Deep Features on Multiple Scales for Coffee Crop
Recognition",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Deep Learning, Remote Sensing, Coffee Crops, High-resolution
Images, Agriculture.",
abstract = "Geographic 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.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.41",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.41",
language = "en",
ibi = "8JMKD3MGPAW/3PFRKFL",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFRKFL",
targetfile = "PID4960341.pdf",
urlaccessdate = "2024, Apr. 28"
}