@InProceedings{OrtegaBerHapGomFei:2019:EvDeLe,
               author = "Ortega, M. X. and Bermudez, J. D. and Happ, P. Nigri and Gomes, 
                         Alessandra Rodrigues and Feitosa, R. Queiroz",
          affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do 
                         Rio de Janeiro (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade 
                         Cat{\'o}lica do Rio de Janeiro (PUC-Rio)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Pontif{\'{\i}}cia 
                         Universidade Cat{\'o}lica do Rio de Janeiro (PUC-Rio)}",
                title = "Evaluation of deep learning techniques for deforestation detection 
                         in the Amazon forest",
            booktitle = "Proceedings...",
                 year = "2019",
         organization = "Photogrammetric Image Analysis",
                 note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
             abstract = "Deforestation is one of the main causes of biodiversity reduction, 
                         climate change among other destructive phenomena. Thus, early 
                         detection of deforestation processes is of paramount importance. 
                         Motivated by this scenario, this work presents an evaluation of 
                         methods for automatic deforestation detection, specifically Early 
                         Fusion (EF) Convolutional Network, Siamese Convolutional Network 
                         (S-CNN) and the well-known Support Vector Machine (SVM), taken as 
                         the baseline. These methods were evaluated in a region of the 
                         Brazilian Legal Amazon (BLA). Two Landsat 8 images acquired in 
                         2016 and 2017 were used in our experiments. The impact of training 
                         set size was also investigated. The Deep Learning-based approaches 
                         clearly outperformed the SVM baseline in our approaches, both in 
                         terms of F1-score and Overall Accuracy, with a superiority of 
                         S-CNN over EF.",
  conference-location = "Munich",
      conference-year = "18-20 Sept.",
             language = "en",
           targetfile = "ortega_evaluatio.pdf",
        urlaccessdate = "10 maio 2024"
}