Sea Ice and Open Water Classification of SAR Images Using a Deep Learning Model
Yibin Ren, Huan Xu, Bin Liu, Xiaofeng Li
Abstract
Accurate and robust classification methods of sea ice and open water are significant for many applications. Synthetic Aperture Radar (SAR) imaging capability is independent of weather conditions and is widely used in sea ice classification. U-Net, a deep learning framework, has achieved great success in the field of biomedical image classification. In this study, we construct a U-Net-based “end-to-end” model to classify the sea ice and open water pixels in SAR imagery. Five SAR images acquired in the Gulf of Alaska near Bering Strait are used in this case study. We manually label the SAR images as ice and water. The labeled images from the first four SAR image are divided into chips to be fed into the U-Net model for training. The fifth SAR image is employed as the testing data. Experiments show that the precision and the recall of the testing image is 91.64% and 91.70%, respectively. Most of the sea ice, including small chunks and sinuous ice edges, can be successfully classified.