## 3 Results
### Ablation Experiments  
To further examine the validity of the FEPA-Net model and verify the influence of each module on model performance, experiments were conducted for each module and accuracy analysis, including (1) U-Net; (2) U-Net+ Feature Extraction Module; (3) U-Net+ Position Attention; (4) U-Net+ Feature Extraction-Position Attention. Under identical conditions of computer hardware, software, and model-related parameters, each of the models in the four experiments underwent training for a duration of 100 epochs. The WHU building dataset was employed for these experiments, and the experimental outcomes are presented in Table 2. Compared with U-Net, adding the feature extraction module can  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Methods** & **OA (\%)** & **Precision (\%)** & **Recall (\%)** & **MIoU (\%)** \\ \hline \multirow{4}{*}{Massachusetts} & PSPNet & 86.78 & 84.64 & 82.76 & 74.83 \\  & DeepLabV3+ & 87.34 & 86.83 & 84.43 & 78.46 \\  & U-Net & 92.96 & 89.19 & 86.52 & 80.06 \\  & FEPA-Net & 94.25 & 90.84 & 87.96 & 81.47 \\ \hline \multirow{4}{*}{WHU} & PSPNet & 90.21 & 95.47 & 92.67 & 88.95 \\  & DeepLabV3+ & 94.69 & 87.89 & 88.25 & 91.58 \\ \cline{1-1}  & U-Net & 97.33 & 96.30 & 95.87 & 93.40 \\ \cline{1-1}  & FEPA-Net & 98.93 & 97.34 & 97.27 & 94.83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the accuracy of different models on the two datasets.  
Figure 9: Comparison results of different models on the WHU aerial imagery dataset. (**a**) Original image. (**b**) Ground truth. (**c**) PSPNet. (**d**) DeepLabV3+. (**e**) U-Net. (**f**) FEPA-Net.  
efficiently enhance the precision of building boundary extraction, and the recall rate is improved by 1.08%. After incorporating the position attention model into the network, there is a notable enhancement in addressing the misclassification issue during building extraction. Specifically, the recall rate was elevated by 0.95%. In terms of the average cross-comparison accuracy, the model in this paper improves by 1.43%, 1.07%, and 0.79% compared to the U-Net infrastructure, the model with only the feature extraction module, and the model with only the position attention module, respectively, which indicates that the module for extracting features and the position attention module improve the outcome of extraction by the model to some extent, and the U-Net+ Feature Extraction- Position Attention (FEPA-Net) model has the highest accuracy.  
From Figure 10, it is evident that the buildings extracted using the U-Net method have missed and wrong detection, while using the U-Net+ Feature Extraction- Position Attention method can extract all of the buildings in the image, which has the best effect on building extraction compared with other methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **OA (\%)** & **Precision (\%)** & **Recall (\%)** & **MIoU (\%)** \\ \hline U-Net & 97.33 & 96.30 & 95.87 & 93.40 \\ U-Net+ Feature Extraction Module & 97.57 & 96.96 & 96.95 & 93.76 \\ U-Net+ Position Attention Module & 98.15 & 96.18 & 96.82 & 94.04 \\ U-Net+ Feature Extraction–Position Attention & 98.93 & 97.34 & 97.27 & 94.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation experiments.  
Figure 10: Comparison results of different module on the WHU building dataset. (**a**) Original image. (**b**) Ground truth. (**c**) U-Net. (**d**) U-Net+ Feature Extraction Module. (**e**) U-Net+ Position Attention. (**f**) U-Net+ Feature Extraction–Position Attention.