## 1 Introduction  
Sea ice covers approximately 7% to 15% of the ocean and is the most important component of the global cryosphere [1]. With global warming increasing in recent years, the areas occupied by different categories of sea ice have decreased to differing degrees [2], which has provided conditions for seasonal alterations to the Arctic navigation channel. The complex sea ice conditions in Arctic waters seriously affect the navigability of the Arctic seaway, especially during the summer sea ice melt and freeze periods, during which the sea ice conditions are serious. At different ice levels, ships have different means of quantifying navigational risk, such as by using the POLARIS system proposed by the International Maritime Organization (IMO) based on sea ice concentration and sea ice category development stage characteristics [3], which all require prior data. Sea ice information, including regarding the sea ice type and sea ice concentration of a region, needs to be obtained in advance. Therefore, sea ice classification during Arctic sea ice monitoring is important and helpful when assessing ice conditions, undertaking sea ice forecasting, and conducting polar research. In recent years, new technologies and methods have emerged for Arctic cryosphere detection. Satellites such as the GNSS-R have a low launch cost and can be used to achieve the low-cost, high-temporal resolution detection of the above-mentioned geophysical elements through the implementation of a highly promising constellation network observation program. Many scholars have carried out related research, for example, via sea ice thickness [4, 5] and sea ice concentration [6] monitoring missions. Moreover, the use of synthetic aperture radar (SAR) images to extract sea ice types is currently the main approach used in research. Traditional machine learning-based sea ice classification methods extract features to distinguish sea ice types based on backscattering, image texture, etc. For example, [PERSON] [7] used the support vector machine and decision tree classification methods based on Radarsat-2 dual-polarization experimental data to obtain open seawater and flat ice information by calculating sea ice textural feature classification maps. [PERSON] proposed a method based on a combination of the Kalman filter, a gray-level concurrence matrix, and the SVM algorithm to classify SAR images. [PERSON] [8] proposed an attribute difference decision tree classification method to be used in considering the original multi-polarization attribute features of images, which improved the accuracy of sea ice classification in full-polarization SAR images. [PERSON] [9] introduced a fully automatic design of a numerically optimized decision tree algorithm and demonstrated its application to sea ice classification using SAR data. In conclusion, these methods require a priori expert knowledge and complex algorithms to extract features. Further, in Arctic Sea ice classification tasks, differences in regions and seasons have a large impact on sea ice classification accuracy, which make it difficult to ensure the fast, frequent, and fully automated delivery of sea ice segmentation maps. Moreover, image classification based on traditional methods cannot extract deep-level features and often misses spatial and textural features that are beneficial for classification. In recent years, various deep learning algorithms have gradually been developed and matured, and deep learning techniques have been widely used in the fields of target detection and image semantic segmentation [10]. Further, many researchers have used deep learning-based full convolutional neural networks to extract sea ice classification information.