## 3 Experiments and Analysis
### Ablation Experiments  
To further evaluate the contribution of each improved part, ablation experiments were conducted using the CHN6_CUG dataset, gradually incorporating the improvements into the baseline model DeepLabV3+. Table 4 presents the experimental results of the baseline model after replacing the backbone network (MobileNetV2), adding the ABPM, FBFS and DFM. In the ablation experiment for the FBFS module, the shallow and deep features from the spatial domain, as well as the high- and low-frequency features from the frequency domain, were concatenated along the channel dimension separately. These concatenated features were then passed through a \(1\times 1\) convolution to adjust the channel dimensions before being fed into the Segmentation Header.  
From the analysis of the ablation experiments, it can be concluded that using MobileNetV2 as the backbone network significantly reduces the number of parameters while improving the segmentation accuracy of the model. Replacing ASPP with the ABPM module increases the parameter count due to the use of high-dimensional features and residual connections, but the precision of road extraction improved significantly. Finally, introducing frequency-domain features further enhances the model's ability to extract roads in shaded areas. Figure 6 shows the improvement in the model's extraction results for roads in shadowed areas after adding frequency domain information.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **mIoU/\%** & **mPA/\%** & **Recall\%** & **Accuracy\%** & **PN/M** \\ \hline DeepLabV3+ & 71.08 & 78.77 & 59.25 & 96.05 & 208.70 \\ +MobileNetV2 & 72.74 & 79.21 & 59.74 & 96.46 & 22.18 \\ +ABPM & 73.65 & 80.81 & 63.08 & 96.52 & 31.76 \\ +FBFS & 74.58 & 81.56 & 64.83 & 96.65 & 55.64 \\ +DFM & 74.83 & 81.86 & 65.10 & 96.70 & 55.57 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation Experiment Results.  
Figure 7: Segmentation results of different methods. GT, Ground Truth.  
The loss changes during the training and validation process for the original DeepLabV3+, the model with the MobileNetV2 backbone, and the final CDFNet are shown in Figure 8. DeepLabV3+ has a larger number of parameters, requiring more training steps to effectively learn the patterns in the data, resulting in a slower convergence of the loss value. Additionally, larger models often have more local minima and saddle points, making the optimization process more challenging, causing the loss value to fluctuate continuously. After replacing the backbone with MobileNetV2, the model structure was lightweighted, and the loss value decreased consistently in the early stages of training. The fluctuation of the loss value was significantly reduced, and the optimization process became more stable. The training and validation loss of CDFNet showed a more stable decline, with the final validation loss also decreasing after model improvement, demonstrating CDFNet's better generalization ability in road extraction tasks.  
From the analysis of the ablation experiments, it can be concluded that using MobileNetV2 as the backbone network significantly reduced the number of parameters while improving the segmentation accuracy of the model. Replacing ASPP with the ABPM module increased the parameter count due to the use of high-dimensional features and residual connections, but the precision of road extraction improved significantly. Finally, introducing frequency-domain features further enhanced the model's ability to extract roads in shaded areas. Figure 9 shows the improvement in the model's extraction results for roads in shadowed areas after adding frequency domain information.  
Figure 8: Training and validation loss variations: (**a**) DeepLabV3+. (**b**) After replacing the backbone network with MobileNetV2. (**c**) CDFNet.