@Article{AdarmePrieFeitAlme:2022:ImDeDe,
               author = "Adarme, Mabel Ortega and Prieto, Juan Doblas and Feitosa, Raul 
                         Queiroz and Almeida, Claudio Aparecido de",
          affiliation = "{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)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Improving Deforestation Detection on Tropical Rainforests Using 
                         Sentinel-1 Data and Convolutional Neural Networks",
              journal = "Remote Sensing",
                 year = "2022",
               volume = "14",
                pages = "e3290",
             keywords = "deep learning, deforestation detection, stabilization, synthetic 
                         aperture radar, time series, , tropical rainforest.",
             abstract = "Detecting early deforestation is a fundamental process in reducing 
                         forest degradation and carbon emissions. With this procedure, it 
                         is possible to monitor and control illegal activities associated 
                         with deforestation. Most regular monitoring projects have been 
                         recently proposed, but most of them rely on optical imagery. In 
                         addition, these data are seriously restricted by cloud coverage, 
                         especially in tropical environments. In this regard, Synthetic 
                         Aperture Radar (SAR) is an attractive alternative that can fill 
                         this observational gap. This work evaluated and compared a 
                         conventional method based on time series and a Fully Convolutional 
                         Network (FCN) with bi-temporal SAR images. These approaches were 
                         assessed in two regions of the Brazilian Amazon to detect 
                         deforestation between 2019 and 2020. Different pre-processing 
                         techniques, including filtering and stabilization stages, were 
                         applied to the C-band Sentinel-1 images. Furthermore, this study 
                         proposes to provide the network with the distance map to 
                         past-deforestation as additional information to the pair of images 
                         being compared. In our experiments, this proposal brought up to 4% 
                         improvement in average precision. The experimental results further 
                         indicated a clear superiority of the DL approach over a time 
                         series-based deforestation detection method used as a baseline in 
                         all experiments. Finally, the study proved the benefits of 
                         pre-processing techniques when using detection methods based on 
                         time series. On the contrary, the analysis revealed that the 
                         neural network could eliminate noise from the input images, making 
                         filtering innocuous and, therefore, unnecessary. On the other 
                         hand, the stabilization of the input images brought non-negligible 
                         accuracy gains to the DL approach.",
                  doi = "10.3390/rs14143290",
                  url = "http://dx.doi.org/10.3390/rs14143290",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-14-03290.pdf",
        urlaccessdate = "09 maio 2024"
}