## 4 Conclusions  
In this study, a fully automated sea ice segmentation process based on deep learning was designed for use in sea ice classification during the Arctic summer ice melt cycle, offering a high-resolution sea ice concentration feature calculation method. The main improvements yielded by our method compared to previous methods are as follows:  
1. A method is proposed for extracting sea ice concentrations using a K-means++ clustering algorithm and fast convolution operation. Since its extraction is based on SAR images and is fast and accurate in real time, the data can reflect the spatial distribution of sea ice very effectively. Compared with the direct introduction of sea ice concentration products obtained based on radiometric input features, this method not only has higher spatial resolution but also matches the time of SAR features. 2. A new fully convolutional neural network DF-UHRNet is proposed, which enables the more effective fusion of high-resolution weak semantic features (focusing on the representation of edges in sea ice) and low-resolution strong semantic features (focusing on the abstract morphology of sea ice) via the design of a dual-scale fusion module. Because a vacuity convolution pyramid module is added to the high-level fusion module, the perceptual field of the convolution kernel can be expanded without any loss of resolution (no feature sampling) and thus, sea ice semantic features can be more effectively extracted. The two fusion modules were carefully designed to facilitate not only the fusion of adjacent scale features, but also to reduce the overall quantity of parameters within the model. 3. The method achieves a fully automated sea ice classification with a full process flow. All processes do not require additional human intervention, and the fully convolutional neural network facilitates end-to-end sea ice semantic segmentation. Thus, it contributes to the fully automated mapping of Arctic sea ice. The DF-UHRNet that we designed, compared with the U-Net, HRNet, and U-HRNet, improves recognition capacity and the number of parameters in the model is optimized, which means that the network has greater portability and extensibility and is more convenient for deployment in projects. From a practical point of view, our method is also applicable to other sea areas. The most likely limitation relates to the area of coverage of the ice charts and the supporting data. However, we can resolve this by replacing the ice chart data; for example, in the Northeast Passage region of the Arctic, the CIS weekly ice map product can be replaced by replacing other regional ice map products published by AARI to generate the training set. In addition, since the weekly ice charts are released every Monday and the relay period of Sentinel-1A/B is 6 days, although the 2020 data appear sufficient for training, the relay period of Sentinel-1 data actually increases to 12 days given that the Sentinel-1B satellite has been offline since 2022, which may result in a data shortage for the periods after 2022. In terms of extraction time, the present method not only quickly extracts sea ice types in the Arctic region during the melt ice period but can also quickly extract sea ice types other than seasonal ones. However, considering the effect of seasonal freezing cycles in the Arctic, the sea ice category may be relatively homogeneous during winter and spring, when ice concentration is close to 100% due to extensive freezing. Sea ice concentration indicates the spatial distribution of sea ice in the region and therefore is not very meaningful to introduce. Therefore, the sea ice concentration feature can be discarded before carrying out deep learning sea ice classification during winter and spring periods.