## 4 Results
### Results and Comparison
#### 4.4.1 Massachusetts Data  
Figure 6 showcases two instances derived from the Massachusetts dataset, following a comparison involving various methodologies. Notably, the proposed approach demonstrates enhanced performance in terms of finer details, as indicated in Figure 6. The corresponding metrics are detailed in Table 2. For ease of observation and analysis, a specific region within the yellow frame was chosen from the image. The extraction outcomes pertaining to this selected area are visualized in Figure 7.  
As exhibited in Table 3, the OA of the DPBFN is significantly higher than that of other models using CNN and integrated transformer architecture. The F1 score and IoU are improved, with an average of 99.22% and 98.35%, respectively. The additional DCPA module can obviously reinforce the performance of decapitation extraction and reduce the misclassification of pixels. However, in the part of depth feature extraction, more scale features can be extracted through the DCPA module, and these improvements help to extract roads (by using the fused features) of different sizes.  
Figure 4: Validation accuracy and IoU the curves of DPBFN from three datasets.  
Figure 5: The legends for results outputting.  
Table 4 outlines the performance evaluation of the Massachusetts test dataset using metrics such as OA, P, R, F1 score, and IoU at breakeven points. Notably, the primary distinctions between the DPBFN and alternative approaches encompass factors such as kernel filter dimensions, the count of convolution layers, and the depth of the network structure. The DPBFN can achieve good performance via the DCPA module, which is an effective way to filter the validation information of road characteristics. The parallel multi-scaled dilated convolution operators can not only enlarge the receptive field without losing the feature resolution, but also build the feature pyramid to capture multi-scaled context information. The attention block can widen the network configuration with channel and spatial attention computed by maxpooling, averagepooling, and MLP, which confirms the theory reported in [35]. A statistical examination of Table 3 reveals that the classification accuracy achieved by the DPBFN surpasses that of U-Net, DeeplabV3+, RoadFormer, Swin-UNet, and TransFuse. Among these methods, the proposed approach demonstrates the highest classification accuracy, boasting an IoU of 66.78%, while U-Net registers the last classification rank with an IoU of 59.57%. As demonstrated in this part, we employ  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Image 1**} & \multicolumn{5}{c}{**Image 2**} \\ \cline{2-11}  & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IoU (\%)** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IoU (\%)** \\ \hline U-Net & 96.85 & 96.95 & 99.15 & 98.04 & 96.72 & 96.72 & 96.77 & 98.78 & 97.76 & 96.57 \\ DeepLabv3+ & 97.06 & 97.13 & 99.79 & 98.44 & 96.93 & 96.88 & 96.93 & 99.79 & 98.34 & 96.74 \\ Swin-UNet & 98.26 & 99.53 & 98.56 & 99.04 & 98.21 & 98.35 & 99.48 & 98.51 & 99.00 & 98.30 \\ TransFuse & 98.17 & 98.81 & 99.28 & 99.04 & 98.10 & 98.32 & 98.88 & 99.35 & 99.11 & 98.25 \\ DPBFN & 98.35 & 99.28 & 99.20 & 99.24 & 98.29 & 98.47 & 99.26 & 99.13 & 99.20 & 98.41 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative evaluation results in Figure 6.  
Figure 6: Different results outputting of five methods from Massachusetts dataset.  
Figure 7: Local amplification results for Figure 6.  
comprehensive statistics from the confusion matrix to elucidate the classification accuracy of these five methods across various experimental regions.