Self-supervised learning reduces labelling requirements for sea ice segmentation in Sentinel-1 SAR imagery

Abstract. Monitoring Arctic sea ice variability is crucial for maritime safety. Synthetic Aperture Radar (SAR) imagery provides an effective means of achieving this through all-weather, day-and-night coverage of the Arctic. Navigation in the Canadian Arctic Archipelago currently relies on operational ice information services, including analyst-derived ice charts, satellite imagery, and ice routing products provided by national ice services. However, the development of machine-learning systems capable of automatically processing large volumes of satellite imagery and accurately identifying ice conditions is constrained by the need for extensive manually labelled datasets. To address this limitation, we developed a self-supervised learning (SSL) approach, which uses unlabelled data to learn general image representations. Specifically, we use Bootstrap Your Own Latent (BYOL), a non-contrastive SSL framework, to pretrain a UNet encoder on unlabelled dual-polarised Sentinel-1 Extra-Wide mode (EW) SAR scenes before fine-tuning with a small set of labelled images. We compare the BYOL-pretrained UNet (called UNet SSL in this study) to four baselines: a control UNet, a fully supervised UNet, a Random Forest classifier, and the Segment Anything Model (SAM). With only three labelled scenes, the BYOL-pretrained UNet achieved higher segmentation accuracy than the fully supervised model trained on seven images, more than twice the number of labelled scenes. The most significant gains occurred in Marginal Ice Zone (MIZ) scenes, where the BYOL-pretrained UNet achieved a Matthews Correlation Coefficient  (MCC) of 0.2087, compared with 0.1685 for the fully supervised UNet trained on seven labelled scenes and 0.1449 for the control model trained on three scenes—representing an MCC increase of approximately 24 % and 44 %, respectively. These improvements were accompanied by a substantial reduction in false negatives and a marked increase in recall, indicating improved discrimination under low-contrast, fragmented floe conditions. Our findings demonstrate that SSL reduces annotation requirements for SAR-based sea ice segmentation, improving model generalisation in both consolidated and fragmented ice conditions. This approach offers a scalable solution to the labelling bottleneck in Arctic monitoring and highlights the potential of BYOL as a general pretraining strategy for SAR-based Earth observation image segmentation.