  SAR despeckling is a key tool for Earth Observation. Interpretation of SAR
images are impaired by speckle, a multiplicative noise related to interference
of backscattering from the illuminated scene towards the sensor. Reducing the
noise is a crucial task for the understanding of the scene. Based on the
results of our previous solution KL-DNN, in this work we define a new cost
function for training a convolutional neural network for despeckling. The aim
is to control the edge preservation and to better filter manmade structures and
urban areas that are very challenging for KL-DNN. The results show a very good
improvement on the not homogeneous areas keeping the good results in the
homogeneous ones. Result on both simulated and real data are shown in the
paper.
