A CNN Based Solution for InSAR Phase Denoising

InSAR phase are affected by noise that impairs the performance of applications such as topography, 3D reconstruction, DEM profile, etc. Therefore a denoising step is fundamental. In the last decades many methods for InSAR phase denoising have been proposed such as Local, Non Local and other kind of filters. Inspired by the great success of deep learning in image denoising, methods relying on convolutional neural networks have been proposed recently. In this work, inspired by the outcomes of an amplitude despeckling filter, a multi-objective neural network for interferometric phase denoising is proposed: a cost function composed of three terms takes into account fringe, edges and statistical preservation. The encouraging results are validated quantitatively and qualitatively on a simulated dataset.