  In recent years, Machine Learning (ML) algorithms have become widespread in
all fields of Remote Sensing (RS) and Earth Observation (EO). This has allowed
a rapid development of new procedures to solve problems affecting these
sectors. In this context, the authors of this work aim to present a novel
method for filtering the speckle noise from Sentinel-1 data by applying Deep
Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs). The
obtained results, if compared with the state of the art, show a clear
improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural
Similarity Index ({SSIM}), by proving the effectiveness of the proposed
architecture. Moreover, the generated open-source code and dataset have been
made available for further developments and investigation by interested
researchers.
