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@InProceedings{CastroFeiRosDiaSan:2017:CoAnDe,
               author = "Castro, Jose Bermudez and Feitosa, Raul Queiroz and Rosa, Laura 
                         Cue La and Diaz, Pedro Achanccaray and Sanches, Ieda",
          affiliation = "{Pontifical Catholic University of Rio de Janeiro} and {Pontifical 
                         Catholic University of Rio de Janeiro} and {Pontifical Catholic 
                         University of Rio de Janeiro} and {Pontifical Catholic University 
                         of Rio de Janeiro} and {National Institute for Space Research}",
                title = "A Comparative Analysis of Deep Learning Techniques for 
                         Sub-tropical Crop Types Recognition from Multitemporal Optical/SAR 
                         Image Sequences",
            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 = "Crop Recognition, Multitemporal Images, Autoencoders, 
                         Convolutional Neural Networks.",
             abstract = "Remote 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.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "2017_SIBGRAPI_BERMUDEZ.pdf",
        urlaccessdate = "2021, Jan. 25"
}


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