Brubacher, Neil2024-07-122024-07-122024-07-122024-07-05http://hdl.handle.net/10012/20719Landfast ice polynyas - areas of open water surrounded by ice - are important features in many Northern coastal communities, and their automated detection from spaceborne synthetic aperture radar (SAR) imagery is positioned to support on-ice travel safety under changing Arctic sea ice and climate conditions. The characteristically small spatial scales and sparse distribution of landfast ice polynyas present key challenges to their detection, and limit the suitability of established methods developed for SAR-based sea ice and open water classification at broader spatial scales. This thesis explores the development of deep learning-based object detection networks for landfast ice polynya detection in dual-polarized C-band SAR imagery, having three main contributions. The first is a characterization of landfast ice polynya signatures and separability in SAR imagery based on datasets of polynyas mapped over several seasons near the communities of Sanikiluaq, NU, and Nain, NL. Results from this analysis highlight the challenging and variable nature of polynya signatures in dual-polarized backscatter intensity, motivating the use of convolutional neural networks (CNNs) to capture relevant textural, geometric and contextual polynya features. The second contribution is the development and evaluation of CNN-based object detection networks for polynya detection, drawing on advancements in the natural-scene small object detection field to address the challenging size and sparsity characteristics of polynyas. A simplified detection network architecture optimized for polynya detection in terms of feature representation capacity, feature map resolution, and training loss balancing is found to reliably detect polynyas with sufficient size and local contrast, and demonstrates good generalization to regions not seen in training. The third contribution is an assessment of detection model generalizability between imagery produced by Sentinel-1 (S1) and Radarsat Constellation Mission (RCM) SAR sensors, illustrating the ability for models trained only on S1 imagery to effectively extract and classify polynya features in RCM despite differences in resolution and noise characteristics. Across regions and sensors, missed polynyas are found to have smaller sizes and weaker signatures than detected polynyas, while false predictions are often caused by boundary areas between smooth and rough landfast ice. These represent fundamental limits to polynya / landfast ice separability in the medium-resolution, dual-polarized C-band SAR imagery used in this thesis, motivating future research into multi-temporal, multi-frequency, and/or higher-resolution SAR imagery for polynya detection. Ongoing and future progress in the development of robust landfast ice hazard detection systems is positioned to support community sea ice safety and monitoring.ensea icesynthetic aperture radardeep learningobject detectionpolynyasdecision supportclimate change adaptationMaster Thesis