@InProceedings{MatosakFonsMare:2023:CaStLS,
               author = "Matosak, Bruno Menini and Fonseca, Leila Maria Garcia and Maretto, 
                         Raian Vargas",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of Twente 
                         (UT)}",
                title = "Deep Learning and Cloudy Optical Time Series: A Case of Study with 
                         LSTM to Map LULC in Pantanal",
            booktitle = "Proceedings...",
                 year = "2023",
         organization = "International Geoscience and Remote Sensing Symposium (IGARSS)",
            publisher = "IEEE",
             keywords = "Clouds, LULC, Machine Learning, Pantanal, Time Series.",
             abstract = "Cloud and cloud shadows are a main source of concern when using 
                         dense time series of optical remote sensing images. Machine 
                         learning has the potential to effortlessly overcome this barrier 
                         using Long Short-Term Memory (LSTM), which is a deep learning 
                         algorithm created to analyze time series and has parts dedicated 
                         to suppress irrelevant information. In this context, we evaluated 
                         the ability of models with LSTM layers to create LULC maps using 
                         either cloudy or gap-filled Landsat-8/OLI time series for 
                         Pantanal. Five different LSTM models were trained with tenfold 
                         cross validation using samples gathered by the authors. Our 
                         results indicate that simple models are more accurate with filled 
                         time series, but this difference in accuracy was not present in 
                         more complex models. We also present a LULC map created for the 
                         entire Pantanal.",
  conference-location = "Pasadena",
      conference-year = "16-21 Jul. 2023",
                  doi = "10.1109/IGARSS52108.2023.10282993",
                  url = "http://dx.doi.org/10.1109/IGARSS52108.2023.10282993",
                 isbn = "979-835032010-7",
             language = "en",
           targetfile = "
                         Deep_Learning_and_Cloudy_Optical_Time_Series_A_Case_of_Study_with_LSTM_to_Map_LULC_in_Pantanal 
                         (1).pdf",
        urlaccessdate = "16 jun. 2024"
}