Deep Learning-Based Optical Flow in Fine-Scale Deformation Mapping of Sea Ice Dynamics

Optical methods deployed for studying motion and deformation of objects often struggle to distinguish small displacements hidden behind observational noise. In geophysical applications, this has limited analysis to lower spatial and temporal resolutions, while reliable extraction of high-resolution data is required for understanding material deformation and failure. In this work, we propose a novel method for determining deformation for noisy observational data using deep learning-based optical flow. To enable higher estimate accuracy, we introduce a novel initialization technique considering contextual information. This allows an unprecedentedly high-resolution description of motion in radar imagery. We use the proposed technique on verification cases to compare with the currently used methodologies and on ship radar observations on sea ice deformation. The outcome of our work is an open-source end-to-end tool for determining full-field Lagrangian deformation fields for data sets with small pixel displacements and high observational noise.