Assessment of deep learning based solutions for sar image despeckling

SAR (Synthetic Aperture Radar) sensors are fundamental tools for the Earth Observation. Actually, SAR images are affected by a multiplicative noise speckle that require a filtering step crucial for further application: classification, detection, etc. As in all image processing task, deep learning has been widely used for SAR image despeckling in the last years. Many methods have been proposed with different architectures, cost functions, training approaches, showing impressive performance. Actually, differently from natural domain denoising, an extensive comparison of such methods is still missing. As matter fact, such methods focus their comparison on few testing images. The aim of this paper is to propose and carry out the comparison among DL (Deep Learning) based methods not only in the testing phase but explointg the validation dataset used during the training for evaluating performance on SAR based metrics. The aim is to give an assessment on a more wide scenarios.