Deep Learning and Cloudy Optical Time Series: A Case of Study with LSTM to Map LULC in Pantanal

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.