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 
            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",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "PID4960341.pdf",
        urlaccessdate = "2021, Jan. 25"