@InProceedings{TaquaryFoMaBeMaSaMu:2021:DeClDe,
               author = "Taquary, Evandro Carrijo and Fonseca, Leila Maria Garcia and 
                         Maretto, Raian Vargas and Bendini, Hugo do Nascimento and Matosak, 
                         Bruno Menini and Sant'Anna, Sidnei Jo{\~a}o Siqueira and Mura, 
                         Jos{\'e} Cl{\'a}udio",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of Twente} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Detecting clearcut deforestation employing deep learning methods 
                         and SAR time series",
            booktitle = "Proceedings...",
                 year = "2021",
         organization = "International Geoscience and Remote Sensing Symposium (IGARSS)",
            publisher = "IEEE",
              address = "Breussels",
             keywords = "Deep Learning, Deforestation, Time Series, Sentinel-1, SAR.",
             abstract = "Automating the systematic monitoring of deforestation in the 
                         Brazilian biomes has become imperative. In this sense, a promising 
                         research field lies upon the exploitation of orbital imaging based 
                         on Synthetic Aperture Radar (SAR) sensors, since this technology 
                         is less affected by cloud cover, allowing systematic data 
                         acquisitions. In addition, the growing availability of with no 
                         charge SAR data products enables investigations on the use of time 
                         series extracted from this category of instruments, paving the way 
                         for more sophisticated temporal analyzes. This work presents the 
                         results of a SAR time series classification model designed to 
                         identify clearcut deforestation patterns in time, through an 
                         Artificial Intelligence approach known as Recurrent Neural 
                         Networks. The classification was performed using 5216 samples of 
                         Sentinel-1 time series within the Amazon basin, reaching an 
                         overall accuracy of 96.74%.",
  conference-location = "Online",
      conference-year = "12-16 July",
             language = "en",
           targetfile = "taquary_2021.pdf",
        urlaccessdate = "09 maio 2024"
}