## 4 Experiments and Analysis
### Experimental Results
#### 4.4.2 Jiangsu Province  
Similarly, we evaluated the performance of seven algorithms on Jiangsu Province images under identical experimental conditions. As shown in Table 2, PSANet achieved superior results in terms of the F1-Score, IoU, and OA, with scores of 83.31%, 71.38%, and 94.18%, outperforming the other six methods. This indicates that PSANet is still able to accurately segment building areas in Jiangsu Province. In addition, although PSANet had a relatively low Recall, its F1-Score remained the highest, and its Precision was only slightly lower than that of DeepLabV3-Plus, indicating that it still has good stability in Jiangsu Province. In particular, we noticed that the IoU in the Jiangsu region is about 2.77% lower than that in the Anhui region, and the F1-Score is about 1.92% lower, which may be due to the influence of multiple factors. On the one hand, the incident angles of the Anhui and Jiangsu regions are 34.89\({}^{\circ}\) and 31.37\({}^{\circ}\), respectively. The difference in incident angles results in distinct radar scattering characteristics, which affect the model's performance in recognizing the building area. On the other hand, the pixel spacing area of the Jiangsu region is 8.92 square meters, which is lower than the 10.68 square meters in the Anhui region. The smaller spacing contains richer details, which helps to identify building areas more accurately but also increases the complexity of the background. Furthermore, the sample size of the Jiangsu region is 3451, which is more than the 1735 samples in Anhui. Generally, a larger sample size can enhance the model's generalization ability. However, considering the potential class imbalance in the data, the model may tend to recognize categories with more samples, thereby overlooking categories with fewer samples.  
Figure 8 shows the segmentation results of four representative images from the Jiangsu Province test set. Upon observation, all seven methods still have a certain degree of segmentation error. The results derived using methods (b) and (e) contained a relatively large number of red pixels, indicating that these methods misclassified many building areas as non-building areas. In particular, we noticed that method (e) showed improved segmentation performance in the Jiangsu region compared to the Anhui region and outperformed method (b). This may be due to the large sample size of the Jiangsu region, which enables the transformer to extract important features from large amounts of data better, thus performing better than traditional CNN methods on larger datasets. Meanwhile, methods (a), (d), and (f) contained a relatively large number of green pixels, indicating these methods misclassified many non-building areas as building areas. Although our method also had some errors, the total number of red and green pixels in the segmentation results was relatively small. In addition, for the low-density and high-density building areas in Jiangsu Province, our method delivered more accurate segmentation results than other methods. From a qualitative assessment perspective, this illustrates the superiority of our method for segmenting building areas in Jiangsu Province.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **IoU** & **Recall** & **Precision** & **OA** & **F1-Score** \\ \hline SegNet & 65.93 & 84.73 & 74.82 & 92.27 & 79.47 \\ DeepLabV3-Plus & 68.52 & 78.04 & 84.89 & 93.67 & 81.32 \\ MAFF-HRNet & 69.75 & 82.28 & 82.08 & 93.71 & 82.18 \\ UNet++ & 69.37 & 83.18 & 80.71 & 93.52 & 81.92 \\ SegFormer & 68.58 & 81.85 & 80.88 & 93.38 & 81.36 \\ PSPNet & 67.86 & 82.72 & 79.06 & 93.08 & 80.85 \\ PSANet (Ours) & 71.38 & 82.23 & 84.39 & 94.18 & 83.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of different methods on Jiangsu Province images (%).