## 5 Experimental Results
### Experimental Settings
#### 5.2.6 Discussion
We deduced from the above curves that our algorithm increased the segmentation metrics by a significant margin, which explains the visible amelioration of the segmented map shown in Figure 11.  
To judge the global efficiency of our algorithm, we compared it with two other domain adaptation algorithms. The first is FCNs in the wild [32]. It is a domain adaptation algorithm applied in general semantic segmentation. The second is the unsupervised algorithm introduced by [PERSON] et al. [4]. It was designed specifically for domain adaptation in semantic segmentation of aerial imagery. We provided in Table 5 the comparison of average accuracy, dice coefficient (F1 score) and IoU in the task of domain adaptation from Potsdam to Vaihingen. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Images from Target** & **Average Accuracy** & **Precision** & **Sensitivity** & **Dice Coef** & **IoU** \\ \hline
0 & 0.334 & 0.335 & 0.334 & 0.288 & 0.169 \\
192 & 0.363 & 0.365 & 0.363 & 0.318 & 0.179 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Segmentation Accuracy, Precision, Sensitivity, Dice Coefficient and IoU score for different numbers of sampled images from the target domain (Vaihingen as source and Potsdam as target). Figure 12: Segmentation accuracy for different numbers of sampled images from the target domain. Figure 14: Segmentation sensitivity for different numbers of sampled images from the target domain. Figure 13: Segmentation precision for different numbers of sampled images from the target domain. Figure 15: Segmentation dice coefficient for different numbers of sampled images from the target domain. As shown in Table 5, our algorithm outperforms the other two algorithms in all measures. As a conclusion, the final user can choose between the unsupervised approach introduced by [PERSON] et al. [4] and the current supervised approach. If the user does not want to invest time in labeling and wants a medium accuracy, he can choose the unsupervised approach. If he cares most about the accuracy, he can use the supervised approach presented in this paper. The choice will be a tradeoff between the labeling cost and the accuracy of domain adaptation. Going deeper in the analysis of our algorithm, we studied its effect on the improvement of the segmentation accuracy for every class apart. Table 6 shows the improvement noted in each class for different numbers of sampled images. Moreover, we can validate that for every class, the more the size of labeled images increases, the more the segmentation accuracy is improved. This has some limited exceptions with some local decrease in the accuracy but did not affect the global improvement behavior. Only one class, the clutter background, knows a small decrease in accuracy and this is because there is no specific pattern to be learned for this class. It represents all the components that cannot be included within the other five classes, and the small number of samples is not sufficient for the first GAN network to capture the different patterns existing for this class. On the other side, classes Trees, Low vegetation and Building know a significant increase of accuracy by a margin of 47.7%, 21.9% and 31.6%, respectively. This is because the picked samples from the target domain were sufficient for the first GAN network to learn most of the patterns that exist for the class. Figure 16: Segmentation IoU for different numbers of sampled from the target domain. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Average Accuracy** & **Dice Coef** & **IoU** \\ \hline Without Domain Adaptation & 0.345 & 0.316 & 0.175 \\ FCNs in the wild & 0.486 & 0.413 & 0.309 \\ Unsupervised Method in [4] & 0.520 & 0.490 & 0.300 \\ Ours (188 images) & 0.588 & 0.572 & 0.349 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Segmentation Accuracy, Dice Coefficient and IoU score for different domain adaptation algorithms (Potsdam as source and Vaihingen as target).