Extracting Quantitative Information on Coastal Ice Dynamics and Ice Hazard Events From Marine Radar Digital Imagery

Marine radars have been employed to gather data in applications that require near-continuous monitoring and tracking of objects over a wide area from a single viewpoint, independent of weather and light conditions. However, little attention has been paid toward utilizing such systems for the study of long-term phenomena and detecting anomalous environmental events or hazards that may occur infrequently but have potentially significant impacts on coastal populations. In this paper, we concentrate on tracking features in seasonally ice-covered Arctic coastal ocean environments. We have developed tools for automated analysis of ground-based radar images of landfast ice and moving sea ice to extract ice-floe trajectories and velocity fields, delineate the boundary of stable landfast ice, detect events relevant to coastal populations and identify surface vessels. We employ dense and feature-based optical flow approaches to compute motion fields from the images, active contours for delineation of stable landfast ice, and Hidden Markov Models for machine learning based event detection. We present results from the analysis of sample images jointly with a quantitative evaluation of algorithm performance relative to operator-based assessments.