## 2 Materials and Methods
### Architecture of DF-UHRNet
#### 2.6.1 Main Body  
The main structure of the DF-UHRNet is shown in Figure 5; it is a U-shaped network structure with a symmetrical structure of encoding and decoding common to fully convolutional neural networks, which enables end-to-end semantic segmentation tasks. The U-Net-based network has certain defects. On the one hand, it employs a skip concatenation approach, which leads to a lack of efficient multi-scale features during feature fusion recovery. This gives rise to the semantic gap problem, which is exhibited by the weak extraction ability of the network in relation to feature boundaries and small objects. In sea ice segmentation problems, the morphological details of sea ice boundaries are particularly important in obtaining good segmentation accuracy. On the other hand, U-Net-based methods use CNNs to obtain information, and CNNs are often unable to make good use of global contextual information due to the limitations of their sensory field size. In this respect, our network adopts a method of improvement similar to that of the U-HRNet network by introducing a fusion module with no more than two resolution branches into the HRNet, so as to avoid the loss of feature details that occurs due to the multiple consecutive upsampling and downsampling operations undertaken in HRNet networks. The advantages of the HRNet are thus maintained, and repeated cross-resolution information transmission can be enacted by retaining multiple resolution streams. By adopting the CBAM attention mechanism, which adjusts feature computations adaptively through the channel attention mechanism and the spatial attention mechanism, it enables unnecessary computations on each resolution feature mapping to be allocated to more meaningful parts to improve the overall semantic presentation. Details can be found in Section 2.6.2.  
As is shown in Figure 5, the structure of the network's main body can be divided into nine stages, where stages 1 and 9 involve feature convolution extraction at the same resolution, while stages 2 to 8 are the fusion modules of the same resolution features, with down- and upsampling. The higher-scale semantic features that result from the fusion process in the encoding part of stages 2 to 4 are fed into the fusion module of the decoding part of stages 6 to 8 through jump connection. The lower-scale semantic features obtained after fusion are then used in the decoding part of stages 5 to 8. The semantic features of sets with four different pixel sizes--16 \(\times\) 16, 32 \(\times\) 32, 64 \(\times\) 64, and 128 \(\times\) 128--are resampled to 256 \(\times\) 256 pixels, the multitude of semantic features are spliced and fused to obtain the final layer (which is the same size as the input features), the information on images with different resolutions is cross-fused, and the activation of the softmax function is then finally followed by the final output.  
Since stage 5 had the strongest semantic features with the lowest resolution, unlike other fusion modules, we introduced Atreus Spatial Pyramid Pooling convolution into the deep fusion module to increase the receptive field, extract a wider range of global contextual information, and output the results directly through the deepest semantic layer in stage 5. A more useful fusion of higher-resolution semantic and lower-resolution global contextual information is obtained by concatenating high- and low-resolution networks in parallel. This enabled us to obtain multi-scale semantic information, after which the segmentation results were post-processed using conditional random fields.