Automatic polar ice thickness estimation from SAR imagery

Global warming has caused serious damage to our environment in recent years. Accelerated loss of ice from Greenland and Antarctica has been observed in recent decades. The melting of polar ice sheets and mountain glaciers has a considerable influence on sea level rise and altering ocean currents, potentially leading to the flooding of the coastal regions and putting millions of people around the world at risk. Synthetic aperture radar (SAR) systems are able to provide relevant information about subsurface structure of polar ice sheets. Manual layer identification is prohibitively tedious and expensive and is not practical for regular, longterm ice-sheet monitoring. Automatic layer finding in noisy radar images is quite challenging due to huge amount of noise, limited resolution and variations in ice layers and bedrock. Here we propose an approach which automatically detects ice surface and bedrock boundaries using distance regularized level set evolution. In this approach the complex topology of ice and bedrock boundary layers can be detected simultaneously by evolving an initial curve in radar imagery. Using a distance regularized term, the regularity of the level set function is intrinsically maintained that solves the reinitialization issues arising from conventional level set approaches. The results are evaluated on a large dataset of airborne radar imagery collected during IceBridge mission over Antarctica and Greenland and show promising results in respect to hand-labeled ground truth.