  Removing speckle noise from SAR images is still an open issue. It is well
know that the interpretation of SAR images is very challenging and despeckling
algorithms are necessary to improve the ability of extracting information. An
urban environment makes this task more heavy due to different structures and to
different objects scale. Following the recent spread of deep learning methods
related to several remote sensing applications, in this work a convolutional
neural networks based algorithm for despeckling is proposed. The network is
trained on simulated SAR data. The paper is mainly focused on the
implementation of a cost function that takes account of both spatial
consistency of image and statistical properties of noise.
