## 4 Experiments and Analysis
### Ablation Experiment
#### 4.5.2 Effect of Different Encoding Networks  
To further analyze the advantages of the Pseudo-Siamese fusion encoding network in building area segmentation, we conducted comparative experiments from two perspectives. On the one hand, to analyze whether the Pseudo-Siamese structure has advantages, we replaced the Pseudo-Siamese fusion encoding network with two networks different from the ResNet type--namely, MobileNetv3 [35] and Xception [36]--in the encoding stage while keeping the rest of the structure basically unchanged. On the other hand, to analyze \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Method** & **IoU** & **Recall** & **Precision** & **OA** & **F1-Score** \\ \hline \multirow{4}{*}{Jiangsu Province} & Base & 66.53 & 76.52 & 83.59 & 93.21 & 79.91 \\  & Proposal 1 & 69.17 & 80.26 & 83.35 & 93.69 & 81.77 \\  & Proposal 2 & 69.51 & 81.61 & 82.42 & 93.68 & 82.02 \\  & Proposal 3 & 71.38 & 82.23 & 84.39 & 94.18 & 83.31 \\ \hline \multirow{4}{*}{Anhui Province} & Base & 69.54 & 79.24 & 85.02 & 95.99 & 82.03 \\  & Proposal 1 & 70.99 & 81.86 & 84.25 & 96.14 & 83.04 \\ \cline{1-1}  & Proposal 2 & 72.49 & 82.55 & 85.61 & 96.38 & 84.05 \\ \cline{1-1}  & Proposal 3 & 74.31 & 84.52 & 86.01 & 96.63 & 85.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The evaluation metrics obtained using different combination methods (%). Figure 9: The segmentation results of building areas using different combination methods: (**a**) Base, (**b**) Proposal 1, (**c**) Proposal 2, and (**d**) Proposal 3.  
the impact of different backbone networks and fusion methods within the Pseudo-Siamese structure on performance, we used deeper versions of ResNet, VGG, and a new fusion method named Sum-Fusion, where feature concatenation was replaced with addition in the fusion encoding algorithm, as well as the fusion method proposed in reference [1], and obtained their evaluation results. Table 4 presents the comparative results, from which it can be observed that the Xception network outperformed the MobileNetV3 network in terms of IoU, Recall, and F1-Score. This indicates that Xception can more effectively extract deep-level features of building areas, thereby improving the accuracy of the segmentation results. Meanwhile, our method outperformed the other two single neural networks in terms of F1-Score, Recall, Precision, OA, and IoU, indicating that its structural fusion from different networks has better performance in building area segmentation compared to single neural networks. In addition, although the IoU and F1-Scores obtained using ResNet18 and ResNet34 are slightly lower than those of ResNet50 and ResNet101 in the Jiangsu Province images, their computational consumption is significantly lower. Furthermore, in the Anhui Province images, the IoU, F1-Scores, and OA obtained using ResNet18 and ResNet34 outperform those of other methods. This indicates that ResNet18 and ResNet34 offer relatively high accuracy while maintaining higher computational efficiency, making them more suitable for practical applications with limited resources. Meanwhile, their excellent performance on the Anhui province dataset indicates better generalization when handling smaller datasets, which further highlights that, although deeper networks can provide higher accuracy, they are not always the optimal choice when considering the computational resources and generalization ability of the resource-limited model. In comparison, VGG13 and VGG16 have similar computational complexity to ResNet18 and ResNet34, but their IoU, Recall, OA, and F1-Score are all lower than the latter, which is likely due to the fact that the VGG series does not use residual connections, which makes them more susceptible to the vanishing gradient problem during training. Therefore, ResNet18 and ResNet34 have more advantages in balancing accuracy and computational efficiency.