## 3 Experiments and Analysis
### Experimental Design  
A total of over 60 scenes from images taken in July-December 2020 were collected and used as training data in this experiment. Before deep learning training, operations such as the temporal matching of ice maps and SAR data and image clipping were performed. The statistics of the categories show that multi-year ice accounted for more than 80% of the data, followed by one-year ice, and the thin ice was represented at the lowest level. To balance the number of samples as effectively as possible, we used data enhancement methods (data flipping, rotating, panning, mapping, random cutting, adding noise, etc.) to broaden the data across a small number of sample categories. As shown in Table 2, in the experimental part of the sea ice classification, the number of samples in each category was kept at 4000. In the extraction of the open water category, we performed weighted binary cross-entropy as the loss function. For the sea ice category, we used the loss function as the cross-entropy loss function. We set the training set and test set segmentation ratios to 0.7 and 0.3, respectively, and the number of training rounds was kept at 50 via the learning rate reduction mechanism (if the loss function does not decrease for five consecutive rounds, the learning rate is halved) and the early stop mechanism (if the learning rate decreases for three consecutive rounds, i.e., fifteen rounds, the training is stopped). Due to the large size of the SAR remote sensing images, at a 50 m spatial resolution, the pixel size of the images per view reached 13,000 \(\times\) 13,000, which undoubtedly placed greater requirements on the computational speed and memory of the device itself. We employed the overlapping edge elimination strategy and rotation prediction and stitched \begin{table}
\begin{tabular}{c c c c c c} \hline
**Feature Name** & **SI** & **BD** & **Feature Name** & **SI** & **BD** \\ \hline \(\sigma_{HH}^{0}\) & 0.18855 & 0.05806 & \(HH_{ma}\) & 0.01155 & 0.03611 \\ \(\sigma_{Hv}^{0}\) & 0.14773 & 0.42784 & \(HH_{ent}\) & 0.02339 & 0.02824 \\ \(\sigma_{HH}^{0}+\sigma_{Hv}^{0}\) & 0.03951 & 0.01751 & \(HH_{ent}\) & 0.01925 & 0.00186 \\ \(\sigma_{HH}^{0}-\sigma_{Hv}^{0}\) & 0.08892 & 0.32102 & \(HV_{mean}\) & 0.20775 & 0.01695 \\ \(\sigma_{HH}^{0}/\sigma_{Hv}^{0}\) & 0.08884 & 0.31103 & \(HV_{std}\) & 0.05596 & 0.49085 \\ \(\left(\sigma_{HH}^{0}-\sigma_{Hv}^{0}\right)/\left(\sigma_{HH}^{0}+\sigma_{Hv} ^{0}\right)\) & 0.03961 & 0.03763 & \(HV_{mean}\) & 0.02176 & 0.18168 \\ \(\left(\sigma_{Hv}^{0}-\sigma_{HH}^{0}\right)/\left(\sigma_{HH}^{0}+\sigma_{Hv} ^{0}\right)\) & 0.03965 & 0.01751 & \(HV_{cont}\) & 0.01894 & 0.14584 \\ \(HH_{mean}\)1 & 0.07868 & 0.20947 & \(HV_{diss}\) & 0.01436 & 0.16384 \\ \(HH_{std}\) & 0.04251 & 0.08298 & \(HV_{homo}\) & 0.03069 & 0.16254 \\ \(HH_{cont}\) & 0.02537 & 0.05249 & \(HV_{ma}\) & 0.01954 & 0.24148 \\ \(HH_{diss}\) & 0.02459 & 0.04681 & \(HV_{asm}\) & 0.03535 & 0.19041 \\ \(HH_{home}\) & 0.02007 & 0.03159 & \(HV_{ent}\) & 0.03105 & 0.25662 \\ \(HH_{asm}\) & 0.01896 & 0.03611 & \(HV_{ent}\) & 0.01155 & 0.24384 \\ \hline \end{tabular}  
* where the subscripts of HH and HV features in _mean_, _std_, _cont_, _diss_, _homo_, _asm_, _ma_, _ent_, _ene_ represent the mean, standard deviation, contrast, homogeneity, dissimilarity, angular second moment, energy, and entropy in the grayscale coeval matrix of the corresponding feature calculation, respectively.  
\end{table} 
Table 1: Results obtained from feature optimization based on the Bhattacharyya distance and separability index for 26-dimensional features. \begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **Category Name** & **Original Samples** & **Augmented Samples** & **Final Samples** \\ \hline Open Water & Open Water & 1690 & 4310 & 6000 \\ Segmentation & Sea Ice & 17,204 & 0 & 6000 \\ Multi-year Ice & 13,780 & 0 & 4000 \\ Sea Ice Classification & One-year Ice & 2388 & 1612 & 4000 \\ Thin Ice & 1029 & 2971 & 4000 \\ \hline \end{tabular}
\end{table}