Deep Learning on SAR Imagery: Transfer Learning Versus Randomly
  Initialized Weights
Morteza Karimzadeh Rafael Pires de Lima
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.