A Deep Learning Method for Arctic Sea Ice Type Classification Based on Active-Passive Microwave Data

Abstract The Arctic-scale classification of sea ice type is important in the fields of macro-monitoring in the Arctic, climate change assessment, and long time-series interannual variability of sea ice. To date, a lack of deep learning-based algorithms for detecting Arctic sea ice types from microwave sensors is comparatively rare. In this study, a sea ice type classification algorithm based on the U-Net-CBAM deep learning semantic segmentation network was developed by carrying out research on sea ice type classification algorithms in the Arctic through the integrated use of ASCAT scatterometer and AMSR2 radiometer data. The results of the study show that the proposed method achieves an impressive average accuracy of 94.1% in extracting sea ice categories using AARI ice maps. The results of the algorithm show a high degree of agreement with the OSI-SAF sea ice type daily products.