InSAR-MONet: Interferometric SAR Phase Denoising Using a Multiobjective Neural Network

Interferometric Synthetic Aperture Radar is an effective and widely adopted tool for earth observation. Based on interferograms it is possible to infer several information about the observed area. Two main problems affecting the interferogram can limit its application: phase noise and phase wrapping. In this paper the attention is focused on the first issue. Several algorithms have been developed for interferogram restoration. Given the wide spread of Deep Learning (DL) in the framework of image processing, DL based algorithms have been proposed for interferogram denoising. Most of the efforts have been devoted in designing specific network architectures or training dataset, rather than on the definition of a specific cost function, well suited for the problem under investigation. The aim of this manuscript is to define a new multi-objective cost function, specifically thought for the interferograms restoration problem: the idea is to provide a cost function able to take into account multiple aspects of the data under investigation (i.e. multi-objective). The cost function is implemented within a Convolutional Neural Network and a specific realistic training dataset is built, to account the main characteristics of real interferograms. The final outcome of the paper is the proposal of a new robust and accurate interferometeric phase denoising algorithm (namely InSAR-MONet ), able to remove undesired noise and, at the same time, able to preserve important phase details. The assessment of the method is conducted on simulated and real datasets, comparing quantitatively and qualitatively InSAR-MONet with the state of the art interferometric denoising algorithms.