Robust Multi-Seasonal Ice Classification from High Resolution X-Band SAR

Kurzfassung

Automated solutions for sea ice type classification from synthetic aperture (SAR) imagery offer an opportunity to monitor sea ice, unimpeded by cloud cover or the arctic night. However, there is a common struggle to obtain accurate classifications year round; particularly in the melt and freeze-up seasons. During these seasons, the radar backscatter signal is affected by wet snow cover, obscuring information about underlying ice types. By using additional spatiotemporal contextual data and a combination of convolutional neural networks and a dense conditional random field, we can mitigate these problems and obtain a single classifier which is able to classify accurately at 3.5 m spatial resolution for five different classes of sea ice surface from October to May. During the near year-long drift of the MOSAiC expedition we collected satellite scenes of the same patch of Arctic pack ice with X-Band SAR with a revisit-time of less than a day on average. Combined with in-situ observations of the local ice properties this offers up the unprecedented opportunity to perform a detailed and quantitative assessment of the robustness of our classifier for level, deformed and heavily deformed ice. For these three classes, we can perform accurate classification with a probability > 95% and calculate a lower bound for the robustness between 85% and 88%.