Super-Resolution of Radargrams with a Generative Deep Learning Model

• Published in: IEEE Transactions on Geoscience and Remote Sensing
• Main Authors: Elena Donini, Lorenzo Bruzzone, Francesca Bovolo
• Other Authors: Donini, Elena, Bruzzone, Lorenzo, Bovolo, Francesca
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2024
• Subjects: Antarc*; Antarctica; Greenland
• Online Access: https://hdl.handle.net/11582/345814; https://doi.org/10.1109/TGRS.2024.3378576; https://ieeexplore.ieee.org/document/10474029

Radar sounder (RS) 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 article 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 cycle generative 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 RS 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.