Identifying Sea Ice Ridging in SAR Imagery Using Convolutional Neural Networks

Abstract

Sea ice ridging presents significant danger to ships navigating Hudson Strait in the winter months. Every winter, ships spend a significant portion of their time beset in sea ice ridges and pressured ice, hindering the advancement of resource development in the Arctic. Manually identifying ridges in satellite imagery is a very laborious process. Combining an extensive Synthetic Aperture Radar (SAR) data set of Hudson Strait with labelled ridge locations spanning nine winters, an automated system can be developed to detect ridging. In this study we test three transfer learning approaches using a popular convolutional neural network (CNN) architecture; DenseNet-161. We find that when some layers of DenseNet-161 are unfrozen, the model can determine if an area is ridged or non-ridged with a receiver operating characteristic curve (ROC-AUC) score of 92.3. This approach outperforms previous work where statistical feature based classification was used to predict ridging on the same data set.