## 2 Materials and Methods
### Architecture of DF-UHRNet
#### 2.6.3 Low-Level and High-Level Fusion Module  
The fusion module within the U-HRNet network can be used to effectively fuse together the low-resolution and high-resolution semantic information, but the presence of more bottleneck blocks in the series leads to huge model parameters, which are at the same time more prone to overfitting. In this experiment, two different fusion modules were designed to extract and fuse feature information applicable to different scales and levels of semantic intensity. After using the residual module to extract features, up- and downsampling was performed, and the splicing operation was used to enact the mutual fusion of high-level and low-level features. The structure of the high-level fusion module is shown in Figure 7a and the structure of the low-level fusion module is shown in Figure 7b. Compared with the low-level fusion module, the high-level fusion module we designed mainly facilitates the extraction and fusion of stronger semantic features at small scales and introduces improved the Atreus Pyramid Pooling Convolution (ASPP) [31] for the extraction of larger-scale semantic features before fusion, which effectively expands the receptive field and can be applied to the information of the whole region. However, the selection of the convolutional expansion rate was particularly important for the ASPP module, and here, we adopted the principle of hybrid dilated convolution (HDC) to set an expansion rate that ensured that the extracted image features could be fully addressed without any voids or missing edges. This process must consider the relationship between the maximum distance between the two non-zero points used, M, and the size of the convolution kernel used, K, as shown in Equation (2):  
\[max[M_{i+1}-2r_{i},M_{i+1}-2(M_{i+1}-2r_{i}),r_{i}]\leq K \tag{2}\]  
where \(r_{i}\) denotes the expansion rate for the expansion rate of the \(i\)-th convolution operation, which must cover all the low-level features and ensure that no convention exceeds 1. Here, convolution dilation rates of 2, 3, and 7 were chosen to enhance the receptive field such that it could cover the entire region, effectively extract sea ice features at different scales, and more effectively extract global contextual information. At the same time, we replaced  
Figure 6: Convolutional Block Attention Module (CBAM).  
the channel fusion of features in ASPP with an ADD operation, which further reduced the overall parameters.  
Overall, the present network achieves the fusion of adjacent scale features by adding fusion modules to the U-Net network after each cycle of resampling. There were seven fusion modules in total, from stages 2 to 8, which could be split into two categories: the low-level fusion module and the high-level fusion module. Compared with the original U-HRNet, which extracts features of the bottleneck block four times, this network uses a single/double basic block, which can effectively reduce the number of network parameters and thus alleviate the overfitting problem. After extracting the features using the basic block, the higher-scale features were downsampled using the lower-scale features that were input for splicing and fusion to derive new lower-scale features, and similarly, the lower-scale features were upsampled with the higher-scale features that were input to derive new fused features. In stages 6 and 7 of the decoder part, the fused features that were extracted from the encoder were fused with the deep semantic features after upsampling, and the jump connections were spliced and fused with the semantic information after the upsampling of the lower-resolution features to derive the new semantic features. The high-level fusion module was used in stage 5 to output the strongest deep semantic features at the lowest scale within the network.