Synthetic aperture radar data analysis by deep learning for automatic sea ice classification

In this paper, we explore the potential of deep learning (DL) networks to produce reliable sea ice classification by analyzing data collected by synthetic aperture radar (SAR) sensors over polar regions. Taking advantage of their ability to extract features from complex datasets, DL schemes can be used to perform large scale data investigation, so to help manual interpretation conducted by experts in sea ice charting services. We highlighted the ability of different DL settings as well as their limits (mainly associated with scarce training data). Experimental results show the validation accuracy and the inference potential of three DL networks.