Classifying Sea Ice Types from SAR Images Using a U-Net-Based Deep Learning Model

Sea ice type's classification plays an essential role in Arctic marine navigation. Synthetic Aperture Radar (SAR) is independent of weather conditions and is widely used in sea ice classification. U-Net is a well-performed deep learning (DL) framework in image classification. This study constructs a U-Net-based “end-to-end” model to classify multi-year ice (MYI), first-year ice (FYI), and open water. We label the SAR images by ice chart provided by the U.S. National Ice Center (USNIC). The labeled images are divided into chips to be fed into the U-Net model for training and testing. Experiments show that the precision and the recall of the testing set are 89.55% and 89.46%. The proposed model can classify sea ice types in an “end-to-end” way with high accuracy.