Edge Preserving CNN SAR Despeckling Algorithm
This addresses the problem of speckle noise in SAR images for Earth Observation, which impairs interpretation, but it is incremental as it builds on a previous solution (KL-DNN).
The paper tackles SAR image despeckling by developing a new cost function for training a convolutional neural network to better preserve edges and filter manmade structures, showing very good improvement in non-homogeneous areas while maintaining performance in homogeneous ones.
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.