## 3 Experiments and Analysis
### Experimental Results
#### 3.3.2 Comparing Experiment Results  
To verify the actual effectiveness of the DF-UHRNet proposed in this study, U-Net, HRNet, and U-HRNet were selected for comparison. Among these, HRNet-W36 and W48 were only selected for comparison with HRNet-W18 because of their poor results in terms of experimental validation. The segmentations and parameter sizes of different models within the same training set can be seen in Figure 12; the data for regions one to five were obtained from the Beaufort Sea, and the sixth and seventh regions, from the Hudson Bay Sea. The detailed parameters can be found in Table 4.  
Our network showed good applicability in sea ice segmentation for all seven of the selected regions, and the identification results it yielded were close to those of the CIS ice charts. The original U-HRNet network contains a vast number of parameters, and after improving its fusion module and adding the attention mechanism, the DF-UHRNet network could reach a greater segmentation accuracy by reducing many of its parameters. The overall recognition accuracy was then further improved upon by using the morphological post-processing results. This is because the huge number of parameters prevented the model from achieving further improvements. In this study, by designing a fusion mechanism and reducing the number of convolutional operations enacted by the fusion module, the model's parameters were optimized. Compared to the UHRNet-Small, the overall quantity of network parameters in the DF-UHRNet was reduced by 20%, while  
Figure 11: The class activation graph output using the Grad-CAM algorithm. The feature visualization between different models is presented horizontally and the feature visualization of different sea ice extraction tasks is presented vertically. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameters** & **OA (\%)** & **MIoU (\%)** \\ \hline HRNet-W18 & 9,671,835 & 82.5 & 72.8 \\ DF-UHRNet (L\_1, H\_2/without ASPP) 1 & 6,128,391 & 82.5 & 73.6 \\ DF-UHRNet (without CBAM) & 5,077,411 & 91.5 & 85.3 \\ DF-UHRNet (without CRF) & 5,079,175 & 87.4 & 79.4 \\ DF-UHRNet (without SIC) & 5,079,175 & 88.7 & 81.7 \\ Our Model & 5,079,175 & 91.6 & 86.5 \\ \hline \hline \end{tabular}  
* where DF-UHRNet (L in L\_1 indicates that the number of convolutional modules of the low-level fusion module is 1 and H\_2 indicates that the number of convolutional modules of the high-level fusion module is 2). Alternatively, we also used a model that lacked the Atreus Pyramid Convolution Module.  
\end{table} 
Table 3: Comparison of ablation experiment precision values. yielding far richer global contextual information, which improved the ability of the network in terms of the extraction and detection of semantic features of sea ice at different scales. We compared the accuracy indices of our model with those of the four other models. As shown in Table 3, our model achieved the best overall accuracy, MIoU, F1-score, and kappa indices when applied to the recognition task for all five regions; these indices improved by 4.36%, 3.78%, 3.12%, and 7.32% and 2.9%, 3.42%, 3.56%, and 5.96%, compared to the U-Net and U-HRNet (Small), respectively. The regions with identification errors were found to primarily exhibit two categories:  
(1) The misclassifications were caused by the similarity of backscattering characteristics among sea ice categories. As shown in region II in Figure 12, the regions of white and gray ice within the multi-year and thin ice areas are more similar in terms of backscattering characteristics, resulting in confusion in identification. (2) This is due to the coarse granularity of the ice chart itself, as a result of which, although the confused and small areas of sea ice were extracted, they were not marked in the ice chart. Such problems can be effectively solved by using our post-processing algorithm, which preserves the edge details as effectively as possible and corrects the confusion between different sea ice categories.