author = "Adarme, Mabel Ortega and Feitosa, Raul Queiroz and Happ, Patrick 
                         Nigri and Almeida, Cl{\'a}udio Aparecido de and Gomes, Alessandra 
          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 {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Evaluation of deep learning techniques for deforestation detection 
                         in the brazilian amazon and cerrado biomes from remote sensing 
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "6",
                pages = "e910",
                month = "Mar.",
             keywords = "deforestation detection, Brazilian biomes, deep learning, optical 
             abstract = "Deforestation is one of the major threats to natural ecosystems. 
                         This process has a substantial contribution to climate change and 
                         biodiversity reduction. Therefore, the monitoring and early 
                         detection of deforestation is an essential process for 
                         preservation. Techniques based on satellite images are among the 
                         most attractive options for this application. However, many 
                         approaches involve some human intervention or are dependent on a 
                         manually selected threshold to identify regions that suffer 
                         deforestation. Motivated by this scenario, the present work 
                         evaluates Deep Learning-based strategies for automatic 
                         deforestation detection, namely, Early Fusion (EF), Siamese 
                         Network (SN), and Convolutional Support Vector Machine (CSVM) as 
                         well as Support Vector Machine (SVM), used as the baseline. The 
                         target areas are two regions with different deforestation 
                         patterns: the Amazon and Cerrado biomes in Brazil. The experiments 
                         used two co-registered Landsat 8 images acquired at different 
                         dates. The strategies based on Deep Learning achieved the best 
                         performance in our analysis in comparison with the baseline, with 
                         SN and EF superior to CSVM and SVM. In the same way, a reduction 
                         of the salt-and-pepper effect in the generated probabilistic 
                         change maps was noticed as the number of training samples 
                         increased. Finally, the work assesses how the methods can reduce 
                         the time invested in the visual inspection of deforested areas.",
                  doi = "10.3390/rs12060910",
                  url = "http://dx.doi.org/10.3390/rs12060910",
                 issn = "2072-4292",
             language = "en",
           targetfile = "adarmel_evaluation.pdf",
        urlaccessdate = "18 jan. 2021"