## 4 Experiments and Analysis
### Experiment III: Performance on the Benchmark Dataset  
In this experiment, the WHU satellite building dataset I [34] is selected as the target domain to verify the effectiveness of our AD-DAF framework. This dataset contains a total of 204 images which are collected from cities over the world. These images are from various remote sensing satellites such as QuickBird, IKONOS, and ZY-3, with the spatial resolution of 0.3 m to 2.5 m. Some example images are shown in Figure 6. Due to the diversity of sensors and imaging conditions in this dataset, it is challenging to extract buildings from this dataset, especially when this dataset is used as the target domain. These 204 images are all used as a test set for the target domain, which is consistent with the number of images used for the experiments in the literature. The pre-trained model still uses the UNet-based model with the source domain of the IAILD dataset already trained in Experiment I.  
Table 3 presents the Recall, Precision, F1, and IoU of our AD-DAF framework with the UNet model on the WHU benchmark dataset. As can be seen from the table, the performance of the UNet model decreases substantially when migrating from the IAILD dataset to the WHU dataset and improves substantially when adding anomaly detection and reclassification to the pre-trained UNet model, with the Recall improving by 18.37%, the F1 improving by 8.7%, and the IOU improving by 9.94%. Furthermore, it can be seen from this table that the results of our AD-DAF framework are close to the testing results without the cross-domain of the WHU dataset, with only a 3.78% reduction in the F1 and 4.44% reduction in the IoU. This indicates that our added anomaly detection and reclassification process can indeed increase the effectiveness of the cross-domain building extraction. Figure 7 shows some building extraction results on the WHU Satellite Dataset I. Visually, the extraction results of our method do not differ much from the ground truth, and the differences are mainly in the building outlines.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Images** & **Methods** & **Recall** & **Precision** & **F1** & **IoU** \\ \hline WHU-\(>\)WHU & UNet [22] & 73.30\% & 73.10\% & 73.20\% & 57.70\% \\ IAILD-\(>\)WHU & Only pre-trained & 55.39\% & 67.19\% & 60.72\% & 43.32\% \\ IAILD-\(>\)WHU & AD-DAF & 73.76\% & 65.56\% & 69.42\% & 53.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results on the WHU satellite building dataset I.  
Figure 6: Some examples of WHU Satellite Dataset I. The lime green color indicates the building outline.  
It should be noted that the pre-trained model based on the IAILD dataset has remained unchanged, while the target domain has changed in Experiments I, II, and III. More importantly, our method has been effective on different target domains yet with the same source domain. However, our AD-DAF framework is not retrained when the target domain changes. In contrast, the current mainstream domain adaptation methods need to be retrained with a large amount of unlabeled data in the new target domain. This further indicates that our AD-DAF framework can be applied to multiple target domains with only one pre-trained model.