Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm
This is an incremental study on optimizing deep learning methods for SAR image denoising, relevant for remote sensing applications.
The paper analyzes how network complexity affects the performance of a convolutional neural network for SAR despeckling, finding that deeper networks generalize better on both simulated and real images.
SAR images are affected by multiplicative noise that impairs their interpretations. In the last decades several methods for SAR denoising have been proposed and in the last years great attention has moved towards deep learning based solutions. Based on our last proposed convolutional neural network for SAR despeckling, here we exploit the effect of the complexity of the network. More precisely, once a dataset has been fixed, we carry out an analysis of the network performance with respect to the number of layers and numbers of features the network is composed of. Evaluation on simulated and real data are carried out. The results show that deeper networks better generalize on both simulated and real images.