## 4 Experiments and Analysis
### Ablation Experiment  
#### 4.5.1 Effect of Different Modules  
In order to assess the effect of each module in PSANet on its performance, the following combination methods were designed:  
* Base: The encoder consisted solely of ResNet34, and the decoder used only a hierarchical decoder to decode the encoded features;
* Proposal 1: The encoder consisted of ResNet34 and ASPP, and the decoder used only a hierarchical decoder to decode the encoded features;
* Proposal 2: The encoder consisted of ResNet34 and ASPP, and the decoder used a hierarchical decoder and FRM to decode the encoded features;
* Proposal 3: The encoder consisted of the Pseudo-Siamese fusion encoding network and ASPP, and the decoder used a hierarchical decoder and FRM to decode the encoded features. Table 3 presents the evaluation metrics obtained using different combination methods, from which it can be observed that Proposal 1 outperformed the Base in terms of the F1-Score, Recall, OA, and IoU in both regions, with a particularly notable improvement of 3.74% and 2.62% in Recall. This indicates that the multi-scale features extracted by ASPP through different receptive fields help the model recognize buildings of different sizes, thereby reducing the under-detection of building areas. Next, compared to Proposal 1, Proposal 2 shows improvements in Recall by 1.35% and 0.69%, IoU by 0.34% and 1.50%, and the F1-Score by 0.25% and 1.01% across the two regions. This indicates that the introduced FRM reduces noise interference and enhances the semantic features of building areas, leading to more accurate building area segmentation. Furthermore, Proposal 3 outperformed Proposal 2 in terms of the F1-Score, Recall, Precision, OA, and IoU. In particular, it achieved an increase of 1.29% and 1.21% in the F1-Scores and an increase of 1.87% and 1.82% in IoU in both regions. This result indicates that the Pseudo-Siamese fusion encoding structure can extract richer feature representations than a single network structure, thereby obtaining more comprehensive contextual information, which, in turn, effectively improves segmentation accuracy and stability. Figure 8: The segmentation results of building areas using different methods on Jiangsu Province images: (**a**) SegNet, (**b**) DeepLabV3-Plus, (**c**) MAFF-HRNet, (**d**) UNet++, (**e**) SegFormer, (**f**) PSPNet, and PSANet (**ours**). Figure 9 shows the segmentation results obtained using different combination methods, from which it can be observed that as each module was progressively added, the false positive rate and false negative rate gradually decreased, as reflected in the changes in the quantity of red and green pixels. Specifically, compared to the Base, Proposal 1 contained fewer red pixels and more green pixels. This is because the introduced ASPP enriches the multi-scale information of deep-level encoded features, thereby improving the recognition of building areas at various scales. However, while recognizing more building areas, relying solely on ASPP makes it difficult to accurately determine the true number of building areas, leading to an increase in false positives. Next, after introducing the FRM, the segmentation result of Proposal 2 contained fewer green pixels than Proposal 1. This indicates that the FRM has a positive impact, particularly in reducing false positives. Furthermore, after replacing ResNet34 with ResNet18-ResNet34, the segmentation results in Proposal3 contain fewer red and green pixels compared to Proposal 2, which is due to the fact that ResNet18, with its shallower network structure, is able to extract more low-level spatial features of the building areas, while ResNet34, due to its deeper architecture, can more effectively extract high-level semantic features. This illustrates that by combining the characteristics of these two networks, it is possible to extract high-level features while also preserving more detailed information about the building areas, thereby reducing false positives and false negatives.