## 2 Materials and Methods
### Extraction of High-Resolution Sea Ice Concentration
#### 2.5.1 Classification of Ice and Water  
Since carrying out a fully automated sea ice classification task based on deep learning requires the preparation of a large amount of training data, this places restrictions on the selection of ice and water segmentation algorithms. It is important to ensure good robustness and extraction speed, while both ensuring algorithmic accuracy and avoiding  
Figure 3: Results of high-resolution sea ice concentration extraction and related processes. (**a**) Open water area extraction based on the deep learning method; (**b**) ice water map extracted based on the K-means++ algorithm; (**c**) the calculated high-resolution sea ice concentration; and (**d**) the results after dimension reduction by PCA.  
any manual intervention in adjusting parameters. The K-means algorithm satisfies these requirements well and has been widely used in studies related to sea ice extraction and classification [22; 23; 24].  
As an unsupervised learning method, the K-means++ algorithm does not require the aid of a priori expert knowledge and can be used to automate the extraction process. It has the advantages of low complexity and high interpretability and has good flexibility in dealing with large data samples. Compared with the traditional K-means method, K-means++ optimizes the selection of the initial class cluster center coordinates by calculating the weighted probability distribution among the class clusters, which partly solves the problem that makes K-means insufficient in the identification of weaker class clusters. However, our algorithm also has certain shortcomings, such as being more sensitive to outliers and abnormal values; the setting of the initial class values, k, and the number of iterations have a large impact on the clustering results and need to be manually pre-specified. Further, its application with non-convex data is not effective.  
In this study, our goal was to accurately extract the details of regional ice water distribution, and so the selection of relevant parameters was equally important. The data were pre-normalized using the HH and HV bands' backscattering intensities of the SAR image data, and the land areas were removed using a land mask before clustering. After applying the clustering algorithm, based on the difference in brightness between sea ice and seawater in the HV band of the SAR images, the final classification results were obtained by calculating the mean value of the clustered data in the HV band, labeling the lower ones as sea ice and the higher ones as seawater.  
The open water in the HH band of SAR images is affected by sea wind, and the sea surface texture there is rougher, while the backscattering is enhanced, which leads to the values of ice and water brightness being similar to each other [25]. Through a large number of experimental observations, the rough seawater on the sea surface is more easily identified as sea ice when the number of clusters, K, exceeds three, and misclassification of ice and water will lead to the overestimation of ice conditions. In this regard, we set the number of clusters, K, as two for ice water extraction and compared several similar methods; the identification results are shown in Figure 4. The Otsu method yields better extraction results when the image distribution presents two peaks, but serious misclassification occurred when the ice was distributed ice and the water was uneven. While the K-means and K-means++ algorithms are similar overall in terms of their recognition of large pieces of ice, K-means++ is better applied in the recognition of details and yields more accurate data for fine and miscellaneous ice, with less noise.