## 4 Experiments and Analysis
### Experimental Results  
#### 4.4.1 Anhui Province  
To demonstrate the advantages of PSANet in building area segmentation, we compared it with six advanced building area segmentation networks: SegNet [29], DeepLabV3-Plus [30], MAFF-HRNet [31], UNet++ [32], SegFormer [33], and PSPNet [34]. All networks were evaluated under identical parameter settings, with the results presented in Table 1. It can be seen that PSANet achieved superior results in terms of the F1-Score, IoU, OA, and Precision, with scores of 85.26%, 74.31%, 96.63%, and 86.01%, outperforming the other six methods. This indicates that PSANet can segment building areas in Anhui Province more accurately than the other methods. In addition, although PSANet had relatively low Recall, its F1-Score and Precision were the highest, indicating that it had better stability in building area segmentation in Anhui Province. Figure 7 shows the segmentation results of four representative images from the Anhui Province test set, with red pixels indicating false negatives and green pixels indicating false positives. The seven methods displayed varying levels of error in segmentation results. Specifically, the results from methods (b), (e), and (f) contained a relatively large number of red pixels, indicating that these methods misclassified many building areas as non-building areas. The errors in methods (b) and (f) may stem from the lack of effective denoising techniques, making it difficult to suppress noise in the SAR data, thereby increasing classification errors. On the other hand, method (e) utilizes a transformer to capture global dependencies in the image, making it effective for processing large-scale regions. However, this also means that when handling complex areas where building regions are intertwined with the background, method (e) may struggle to accurately distinguish these differences, leading to classification errors. Meanwhile, methods (a), (c), and (d) contained a relatively large number of green pixels, indicating that these methods misclassified many non-building areas as building areas. The errors in methods (a) and (c) may stem from the use of a symmetric encoder-decoder architecture, which may result in excessive spatial smoothing during feature extraction, leading to the loss of detailed information. In addition, method (c) enhances feature extraction at different scales through a multi-scale feature fusion mechanism. However, in SAR images, the reflection characteristics of background areas are similar to those of buildings at certain scales, leading to the misclassification of these areas as building areas. Although our results contain some errors, which may stem from the model's insufficient sensitivity to certain variations in the input data, such as noise or slight changes in pixel values, the total number of red and green pixels remains relatively small. In addition, by enhancing the reconstruction of building area details and the extraction of deep-level features of building areas, our method demonstrates superior segmentation performance for all building types in Anhui Province compared to other methods. This illustrates that our method outperforms others in segmenting building areas in Anhui Province. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **IoU** & **Recall** & **Precision** & **OA** & **F1-Score** \\ \hline SegNet & 69.24 & 85.33 & 78.59 & 95.62 & 81.82 \\ DeepLabV3-Plus & 71.51 & 82.22 & 84.59 & 96.22 & 83.39 \\ MAFF-HRNet & 72.36 & 86.03 & 81.99 & 96.21 & 83.97 \\ UNet++ & 71.59 & 83.39 & 83.49 & 96.18 & 83.44 \\ SegFormer & 70.81 & 83.87 & 82.17 & 96.02 & 82.91 \\ PSPNet & 70.76 & 80.84 & 85.02 & 96.14 & 82.88 \\ PSANet (Ours) & 74.31 & 84.52 & 86.01 & 96.63 & 85.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of different methods on Anhui Province images (%).