@Article{TorresTuVeFeSiMaAl:2021:DeDeFu,
               author = "Torres, Daliana Lobo and Turnes, Javier Noa and Vega, Pedro Juan 
                         Soto and Feitosa, Raul Queiroz and Silva, Daniel E. and Marcato 
                         J{\'u}nior, Jos{\'e} and Almeida, Cl{\'a}udio Aparecido de",
          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 
                         {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Universidade Federal do Mato Grosso do Sul (UFMS)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Deforestation detection with fully convolutional networks in the 
                         Amazon forest from Landsat-8 and Sentinel-2 images",
              journal = "Remote Sensing",
                 year = "2021",
               volume = "13",
               number = "24",
                pages = "e5084",
                month = "Dec.",
                 note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
             keywords = "Amazon biome, Change detection, Deep learning, Fully convolutional 
                         neural networks, Remote sensing, Semantic segmentation.",
             abstract = "The availability of remote-sensing multisource data from 
                         optical-based satellite sensors has created new opportunities and 
                         challenges for forest monitoring in the Amazon Biome. In 
                         particular, change-detection analysis has emerged in recent 
                         decades to monitor forest-change dynamics, supporting some 
                         Brazilian governmental initiatives such as PRODES and DETER 
                         projects for biodiversity preservation in threatened areas. In 
                         recent years fully convolutional network architectures have 
                         witnessed numerous proposals adapted for the change-detection 
                         task. This paper comprehensively explores state-of-the-art fully 
                         convolutional networks such as U-Net, ResU-Net, SegNet, 
                         FC-DenseNet, and two DeepLabv3+ variants on monitoring 
                         deforestation in the Brazilian Amazon. The networks performance is 
                         evaluated experimentally in terms of Precision, Recall, F1-score, 
                         and computational load using satellite images with different 
                         spatial and spectral resolution: Landsat-8 and Sentinel-2. We also 
                         include the results of an unprecedented auditing process performed 
                         by senior specialists to visually evaluate each deforestation 
                         polygon derived from the network with the highest accuracy results 
                         for both satellites. This assessment allowed estimation of the 
                         accuracy of these networks simulating a process in nature and 
                         faithful to the PRODES methodology. We conclude that the high 
                         resolution of Sentinel-2 images improves the segmentation of 
                         deforestation polygons both quantitatively (in terms of F1-score) 
                         and qualitatively. Moreover, the study also points to the 
                         potential of the operational use of Deep Learning (DL) mapping as 
                         products to be consumed in PRODES.",
                  doi = "10.3390/rs13245084",
                  url = "http://dx.doi.org/10.3390/rs13245084",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-13-05084-v2.pdf",
        urlaccessdate = "28 abr. 2024"
}