Detection of Landfast Ice Polynyas from SAR Imagery Using Deep Learning

Landfast ice polynyas are important features near many northern coastal communities, and their automated detection from synthetic aperture radar (SAR) imagery is positioned to support on-ice travel safety under changing Arctic sea ice conditions. This research leveraged original datasets of over 5,000 Sentinel-1 SAR observations of wintertime polynyas mapped near the Canadian communities of Sanikiluaq and Nain to investigate deep learning-based landfast ice polynya detection. The Faster-RCNN object detection network was optimized for polynya detection through several modifications to network design elements and training strategies. Resulting detection models generalized well between regions and accurately detected polynyas with local backscatter contrasts above 5 dB, e.g. achieving 90% target recall at 24% precision. Polynyas smaller than 500 meters and with local backscatter contrasts less than 3 dB, constituting approximately half of all observations, were frequently missed. Precision scores below 30% were consistently incurred in attempts to optimize recall. Results highlight challenges to the consistent performance of single-image detectors due to variable and frequently weak polynya signatures in dual-polarized Sentinel-1 SAR backscatter. Future investigations into multi-temporal, multi-frequency, and/or higher-resolution SAR imagery could further support the delivery of robust hazard detection systems relevant to community sea ice safety and monitoring.