## 3 Experiments and Analysis
### Experimental Results
#### 3.3.1 Ablation Study  
To demonstrate the effectiveness of the improvements made here to the network used for the sea ice segmentation task, an ablation experiment was designed, and the final layer of network results was visualized and output using the Grad-CAM algorithm. The Gradient-weighted Class Activation Mapping (Grad-CAM) [35] technique offers a better visual representation of the deeply connected neural network, thus yielding a view that enables a more favorable interpretation of the CNN network when performing segmentation. Using this algorithm, we could compare the results yielded before and after the introduction of the attention mechanism for two types of tasks.  
In Figure 11, the first row refers to open water segmentation, where the traditional U-HRNet was used; the class activation results of this method are more focused on the sea ice region compared to the HRNet, in which attention was enhanced after the introduction of the attention mechanism of CBAM. The second row refers to the sea ice depth segmentation task. Since large inter-ice channels emerge in the summertime, and these inter-ice channels are not marked individually in the CIS peri-ice map, they are regarded as components of other categories of sea ice. They also offer important representations of each category and following the introduction of the sea ice concentration feature, the HRNet can be made to focus more on sea ice regions, i.e., regions with higher sea ice concentrations. The traditional U-HRNet approach focuses on regions with lower sea ice concentrations, where textural changes are more obvious, and the features here were enhanced after the introduction of CBAM. As such, after introducing the sea ice concentration feature, the focus of the whole network could be effectively directed towards regions with higher sea ice concentrations, and this facilitated the segmentation of the summer sea ice category, i.e., by focusing on the whole region rather than just the sea ice region. As such, the experimental effect was improved. Features derived both with and without CBAM were also introduced and were compared using Grad-CAM; we found that by introducing CBAM, attention was effectively directed towards the sea ice region, and the effect of the segmentation was thus effectively improved. Longitudinally, a given network model will differ in terms of its information, which focuses on one of two different types of segmentation tasks--open ocean recognition focuses more on identifying sea ice regions, while sea ice classification focuses on the whole representational information of the region.  
To verify the effectiveness of the two fusion modules, as well as CBAM and the ASPP module, when used in sea ice classification, we conducted ablation experiments using the same training data and experimental parameters, and the results are shown in Table 3. Our model showed significant improvements compared to the HRNet and to the model using only one fusion module, the model with CBAM, that with CRF post-processing, and that with high-resolution sea ice concentration features, especially in the MIoU parameters, which improved by 13.7%, 12.9%, 0.6%, 7.1%, and 4.8%, respectively.