Super-Resolution of Radargrams with a Generative Deep Learning Model

Radar sounder profiles are essential for imaging the subsurface of planetary bodies and the Earth as they provide valuable geological insights. However, the limited availability of high-resolution radargrams poses challenges. This paper proposes a novel method based on generative models to super-resolve radargrams. Our approach addresses the ill-posed and ill-conditioned nature of the super-resolution problem by training a neural network to learn the correlation between radargrams at different scales. The network learns a proxy for the mapping function between ambiguous low-resolution radargrams and more detailed high-resolution ones, considering the data’s geological and statistical properties. The mapping function enables the super-resolution of previously unseen low-resolution radargrams acquired in comparable conditions to those in the training and imaging similar underlying geology. To achieve this, we adopt a generative Cycle-Consistent Adversarial Network (CycleGAN), explicitly designed to match properties between low- and high-resolution radargrams, accounting for variations in dimensions and radiometric properties. Furthermore, we enhance the network performance by incorporating skip connections, a ResNet module, and attention mechanisms. The proposed method is validated using MCoRDS3 radargrams acquired in Greenland and Antarctica as high-resolution data. As low-resolution data, we used simulated radargrams representing what is expected by an Earth-orbiting low-resolution radar sounder to have a controlled experiment. The results are evaluated qualitatively and quantitatively, focusing on the areas with reflections with complex shapes that may generate artifacts and unrealistic geological features.