## 2 Materials and Methods
### Overall Process of Sea Ice Image Segmentation  
We automated the analysis of the summer ice charts with a 50 m spatial resolution by designing a fully automated sea ice segmentation process for summer with an improved deep learning semantic segmentation technique for overall sea ice categories. The overall sea ice classification information extraction process is shown in Figure 2.  
As ideal experimental data, the contents of the weekly CIS ice charts effectively reflect the sea ice categories that occur in the area during summer, but one faces several problems when these are used directly for deep learning training, including the following: (1) The CIS weekly ice charts are released every Monday, and there is thus a gap between the time of acquiring the SAR images and their actual current condition. Further, the sea ice categories during the summer melt and freeze periods change very fast, so there is often a mismatch in the boundaries of the areas. This is particularly true for the boundary between thin ice and open ocean. (2) Since the sea ice concentration information contained in the CIS weekly ice charts offers a regional average based on the segmentation results, it cannot be used as a training set. (3) A large number of ice lanes develops during the summer ice melt period for thin summer ice and multi-year ice, which are not marked on the ice maps as important sea ice characterization information, and there are obvious differences in the characterization  
Figure 1: The study area and the typical sea ice types covered in summer in this area, in the form of the type of sea ice development stage. between ice lanes with open ocean and other sea ice categories. (4) The results of the ice map interpretation are rough, and other categories of sea ice are often mixed in. To improve and optimize the classification process, we must address these problems. The features of bright white sea ice and dark gray open water are easily distinguishable in both optical and SAR images, but making such a distinction between different sea ice types is much more difficult. Meanwhile, due to the influences of the season, temperature, and ocean circulation, the sea ice in summer is also characterized by large areas of inter-ice channels that fit into all sea ice categories, in addition to the presence of open waters, and these inter-ice channels are also an important feature to use in the classification of sea ice categories during the summer melt. Therefore, we improved the sea ice segmentation process to enable us to better perform sea ice characterization during the Arctic ice melt period, thus ensuring the mapping results cohered more closely to the interpretation of the CIS ice charts. The whole segmentation process was divided into two parts: large-area extraction of open water and sea ice segmentation by type. In terms of the space-time mismatch between the ice maps and the source data, we kept this difference to less than 1 day, while the ice charts were released every Monday and the acquired SAR images were collected from Sunday to Monday. From the spatial point of view, the most commonly mismatched areas were found to be open ocean and various types of boundaries, and so the boundary areas were discarded. In terms of sea ice concentration, if other related products are introduced directly, not only will the difference in spatial resolution be large, but the extraction process will also become more complex. In this regard, we extracted high-resolution sea ice concentration features and first used the ice water distribution details extracted by the K-means clustering algorithm to analyze them. We then extracted further high-resolution sea ice concentration features via a sea ice concentration calculation method and then compressed and reduced the scale of the data channel via principal component analysis (PCA) [17], which was then used as the input in the sea ice classification. The extraction results are shown in Figure 3.  
Figure 2: Overall process of automatic extraction of sea ice classification information.