## 3 Results  
### Experimental Results and Analysis  
To efficiently assess the performance of the F EPA-Net model proposed in this paper, the F EPA-Net model is contrasted with the U-Net, DeepLabV3+, and PSPNet networks. Additionally, it is respectively verified on the test sets of the Massachusetts dataset and the WHU aerial image dataset. All network models in the experiments have the same operating environment and the same network optimization parameters.  
This study can verify the validity of the model under the large-scale complex regions by the Massachusetts dataset. The numerous morphologically complex buildings in the image can test the effectiveness of the application of the model, and the outcomes of the test are presented in Figure 8. It can be clearly seen from it that the F EPA-Net model can accurately extract buildings at different scales by discriminating features with contextual information, and it can retain the edges of buildings well. In contrast, the F EPA-Net model is capable of precisely recognizing the neighboring small buildings, and the edges are clearer. When there are large buildings within the image, the large buildings that are obtained by the PSPNet and DeepLabV3+ models are incomplete, with voids inside and blurred edges, and the U-Net model can solve the above problems to some extent but still cannot accurately extract large buildings. In comparison, the F EPA-Net model demonstrates the most optimal extraction performance. It not only retains the building edges during the process of building extraction but also alleviates the problem of missed extractions.  
The shape of the WHU aerial imagery dataset is regular. To further verify the validity of the FEPA-Net model, the results of building extraction using the PSPNet, DeepLabV3+, U-Net, and FEPA-Net models are compared in this paper based on the WHU dataset, as shown in Figure 9. The overall analysis shows that the extraction effect of the FEPA-Net model is better than the other methods. Compared with the PSPNet and DeepLabV3+ models, the buildings extracted by the U-Net and FEPA-Net models are more complete; in particular, the FEPA-Net model is capable of precisely extracting the edges of buildings, and the model enhances the situation of misclassification and omission of small buildings, can finely extract adjacent buildings, and addresses the issue of internal voids in building extraction. Regarding large buildings, every method is capable of extracting the main structure of the building. However, when compared to other methods, the FEPA-Net model extracts the building edges with greater clarity.  
Table 1 shows the accuracy comparison results of different models on different building datasets. It can be seen that the PSPNet model has the lowest extraction accuracy, and the FEPA-Net model has the best four accuracy indexes compared with the comparison models, in which the overall accuracy and the average cross-merge rate on the Massachusetts dataset reach 94.25% and 81.47%, respectively, compared with the U-Net. The overall accuracy and the average cross-merge rate on the Massachusetts dataset reached 94.25% and 81.47%, respectively, which are 1.29% and 1.41% higher than U-Net. On the WHU building dataset, when compared with all other methods, the FEPA-Net model attained the highest degree of precision, with the overall accuracy and average cross-merge rate reaching 98.93% and 94.83%, respectively, which are 1.6% and 1.43% better than the extraction outcomes yielded by the U-Net model, respectively.  
Figure 8: Comparison results of different models on the Massachusetts building dataset. (**a**) Original image. (**b**) Ground truth. (**c**) PSPNet. (**d**) DeepLabV3+. (**e**) U-Net. (**f**) FEPA-Net.