Automatic Estimation of Ice Bottom Surfaces from Radar Imagery
This addresses the need for automated ice sheet subsurface mapping to support climate change research, representing an incremental improvement over manual methods.
The paper tackles the problem of manually converting noisy ground-penetrating radar imagery into 3D ice-bottom surfaces, which is impractical at continental scales, by proposing a computer vision-based technique using probabilistic graphical models and discrete energy minimization, achieving evaluation on 7 topographic sequences with over 3000 radar images each against human-labeled ground truth.
Ground-penetrating radar on planes and satellites now makes it practical to collect 3D observations of the subsurface structure of the polar ice sheets, providing crucial data for understanding and tracking global climate change. But converting these noisy readings into useful observations is generally done by hand, which is impractical at a continental scale. In this paper, we propose a computer vision-based technique for extracting 3D ice-bottom surfaces by viewing the task as an inference problem on a probabilistic graphical model. We first generate a seed surface subject to a set of constraints, and then incorporate additional sources of evidence to refine it via discrete energy minimization. We evaluate the performance of the tracking algorithm on 7 topographic sequences (each with over 3000 radar images) collected from the Canadian Arctic Archipelago with respect to human-labeled ground truth.