## 4 Results
### Results and Comparison
#### 4.4.3 GF-2 Data  
We employed the DPBFN on GF-2 data, as illustrated in Figure 10, with the yellow rectangles strategically, as depicted in Figure 11. Two distinct images were chosen, each from different settings--rural and urban areas. The initial row of Figure 10 features a test image encompassing rural regions, where the roads are narrow and share a texture reminiscent of cultivated land. Moving to the second row in Figure 10, and further highlighted in the zoomed-in depiction in Figure 11, the image covers an urban expan \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Methods** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IOU (\%)** \\ \hline \multirow{4}{*}{Deep Globe} & U-Net & 89.95 & 65.26 & 87.78 & 74.16 & 60.73 \\  & RoadFormer [23] & - & 85.80 & 83.2 & 84.50 & 73.10 \\  & DeepLabv3+ & 90.46 & 66.40 & 81.30 & 72.82 & 61.65 \\  & Swin-UNet & 93.07 & 81.67 & 71.58 & 76.29 & 61.67 \\  & TransFuse & 94.37 & 86.42 & 79.69 & 82.92 & 70.83 \\  & DPBFN & 95.25 & 86.27 & 86.56 & 86.42 & 72.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Various compared results in the Deep Globe Road test dataset. Figure 10: Different results outputting of five methods from the GF-2 dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Image 1**} & \multicolumn{4}{c}{**Image 2**} \\ \cline{2-11}  & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IOU (\%)** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IOU (\%)** \\ \hline U-Net & 91.65 & 90.95 & 99.11 & 94.85 & 90.21 & 93.59 & 93.00 & 99.91 & 96.33 & 92.92 \\ DeepLabv3+ & 92.19 & 91.48 & 99.24 & 95.21 & 90.85 & 92.42 & 91.74 & 99.87 & 95.64 & 91.64 \\ Swin-UNet & 94.74 & 97.39 & 94.87 & 96.11 & 94.27 & 95.92 & 97.93 & 97.75 & 97.83 & 96.43 \\ TransFuse & 94.88 & 97.69 & 96.30 & 96.99 & 94.16 & 96.90 & 97.68 & 98.88 & 98.27 & 96.61 \\ DPBFN & 95.76 & 97.46 & 97.51 & 97.49 & 95.10 & 97.38 & 97.71 & 99.38 & 98.54 & 97.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative evaluation results in Figure 8.  
In the GF-2 dataset, the DPBFN was compared with U-Net, DeeplabV3+, Swin-UNet, and TransFuse. Among the aforementioned methods, U-Net and DeeplabV3+ are representatives of the classic convolution neural network methods. SwinUnet and TransFuse are typical transformer-based and hybrid-based models, respectively, originally used for medical image segmentation. We present the visualization outcomes generated by the DPBFN and the aforementioned representative methods. Pertaining to the comprehensive accuracy analysis encompassing all images within the test set, Table 8 displays the metrics computed from the test set at the breakeven point. We present the visualization outcomes generated by the DPBFN and the aforementioned representative methods. Pertaining to the comprehensive accuracy analysis encompassing all images within the test set, Table 8 displays the metrics computed from the test set at the breakeven point. The hybrid-based method ranks first (with a recall of 65.05%), followed by Swin-UNet (with a recall of 63.23%), Deeplabv3+ (with a recall of 78.38%), and U-Net (with a recall of 75.11%). However, in the GF-2 dataset, the advantages of the DPBFN are not obvious with TransFuse in the respect of other metrics. Although this approach represents a significant improvement compared to other methods, it does not confer an advantage over Transfuse. Further investigation is needed to elucidate the underlying reasons for the phenomenon.