Differentiating Vessel and Iceberg with CNN Using SAR Imagery for Arctic Navigatability

Supply chain disruptors such as piracy and navigational obstacles like icebergs, pose a probable risk to economic development and national security. With the warming of the Arctic Sea ice due to intense changes in climatic conditions, the Arctic and the untapped natural resources that reside in the icy waters are becoming more accessible and prone to security risks. Resulting in more traffic throughout the once inaccessible region. This has made the Arctic region a geopolitical hotspot, with nations vying for the superiority of its resources. Icebergs are a navigational risk to vessels as collisions could result in delayed shipments, monetary costs due to vessel damage, and human health risks. Thus, a robust model must be developed to identify and discriminate between icebergs and vessels to aid in navigational efficacy and increase maritime domain awareness. This paper leverages a novel convolutional neural network (CNN) that employs Synthetic Aperture Radar (SAR) to differentiate between icebergs and vessels in Iceberg Alley. The 5,000-image dataset came from the Statoil and the Centre for Cold Ocean Resources Engineering (C-CORE) collaboration. The dataset consists of SAR imagery, in the HH+HV polarizations. The developed model showed promising results for iceberg and vessel classification, achieving near 90% accuracy on the prescribed dataset. Additionally, the developed model is compared to other existing model architectures to compare model efficiency. This work has a direct influence on navigational safety and transferable applicability to other emerging national security threats in the maritime domain.