  Deploying deep learning on Synthetic Aperture Radar (SAR) data is becoming
more common for mapping purposes. One such case is sea ice, which is highly
dynamic and rapidly changes as a result of the combined effect of wind,
temperature, and ocean currents. Therefore, frequent mapping of sea ice is
necessary to ensure safe marine navigation. However, there is a general
shortage of expert-labeled data to train deep learning algorithms. Fine-tuning
a pre-trained model on SAR imagery is a potential solution. In this paper, we
compare the performance of deep learning models trained from scratch using
randomly initialized weights against pre-trained models that we fine-tune for
this purpose. Our results show that pre-trained models lead to better results,
especially on test samples from the melt season.
