## 3 Results and Analysis
### Ablation Experiments  
1. GF2-FC road dataset  
In this paper, for model training on the GF2-FC dataset, the image size of the training and test sets is uniformly set to \(400\times 400\) pixels, and the output results are ensured to be binarized images. All networks were set to the same experimental environment and parameters for a fairer comparison. Firstly, the unaltered MSNet network structure is used as a benchmark; then, the SCConv and SAFMM modules are added separately, and finally, the ablation experiments are conducted with the combination of MSNet, SCConv, and SAFMM.  
From the results of the ablation experiments on the GF2-FC dataset in Figure 7, it can be seen that Figure 7c is the unimproved MSNet network model, which has incoherent road extraction results, and is also susceptible to the interference of buildings, etc., resulting in poor extraction accuracy. Figure 7d shows the addition of the SAFMM module, which has greatly improved the road connectivity, but the road boundary is vaguer. Figure 7e shows the adding of the SCConv module; the road extraction accuracy is significantly improved, and the breaks are reduced, but there are more false extraction results. Figure 7f shows the network model used in this chapter; after adding the SCConv and SAFMM modules, the extracted road information is also the most complete compared to all the effect diagrams, and the road connectivity has a greater improvement and is less susceptible to interference such as buildings, and the road boundaries are clearer.  
As can be seen in Table 1, the MSNet model without the addition of the SCConv and SAFMM modules is the lowest in each evaluation metric, with 94.84% OA, 79.91% precision, 63.12% recall, 70.18% F1-score, and 54.06% IoU. After incorporating the SAFMM module, all evaluation metrics improved. After adding the SCConv module, although each evaluation metric also improved, the improvement is relatively limited compared to the original model. The last row of the table shows the network SC-FMNet proposed in this paper; after adding both the SCConv and SAFMM modules, the road extraction effect is significantly improved, and each evaluation index reaches the highest value; compared with the original model of MSNet, OA improves by 3.73%, precision improves by 0.83%, recall improves by 9.35%, the F1-score improves by 5.83%, and IoU improves by 7.24%. By inserting both the SCConv and SAFMM modules into MSNet, road information can be extracted from remote sensing images more accurately.  
As can be seen in Table 2, the MSNet model without the addition of the SCConv and SAFM modules has the lowest of all evaluation metrics, with an OA of 95.46%, a precision of 78.83%, a recall of 62.91%, an F1-score of 66.93%, and an IOU of 56.00%. With the addition of the SAFM module, there is a large improvement in all evaluation metrics. However, after adding the SCConv module, the improvement is not obvious. The last row of the table shows the network SC-FMNet proposed in this paper; after adding both the SCConv and SAFM modules, all the indexes reach the optimal results; compared with the original model of MSNet, OA improves by 0.7%, precision improves by 0.77%, recall improves by 3.95%, and the F1-score and IOU improve by 5.68% and 7.00%, respectively. Combining the SCConv and SAFM modules can further improve the segmentation performance of the network model and enhance the segmentation effect.