## 5 Experimental Results
### Experimental Settings
#### 5.2.6 Discussion  
The results of segmentation are always better using the generated image than the original one. Figure 11 shows the result of segmentation of some selected images from the target domain before and after application of our proposed algorithm. We can see clearly that our algorithm improves the quality of segmentation without needing too much data. The results in Figure 11 are generated using first GAN network trained only on 23 labeled images of size \(512*512\) from the target domain. The improvement in segmentation accuracy increases with the number of labeled images picked out from the target domain. We tested different trainings of the GAN 1 model using 1, 3, 12, 23, 47, 94 and 188 labeled images from the target domain. To select the source dataset from Vaihingen and Potsdam, we applied our algorithm twice. The first is done using Potsdam as source and Vaihingen as target, results are shown in Table 3. The second is done using Vaihingen as source and Potsdam as target, results are shown in Table 4. Both tables show the improvement made by the algorithm in segmentation accuracy, precision, sensitivity, dice coefficient and IoU for the different numbers of sampled images from the target domain. Figure 10: Translation of images from the label domain to the source domain using the second GAN network. Figure 11: Segmentation of images from the target domain before and after application of our proposed algorithm. \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 & 0.345 & 0.345 & 0.345 & 0.316 & 0.175 \\
1 & 0.368 & 0.445 & 0.368 & 0.360 & 0.176 \\
3 & 0.422 & 0.470 & 0.422 & 0.404 & 0.214 \\
12 & 0.474 & 0.513 & 0.474 & 0.455 & 0.253 \\
23 & 0.507 & 0.541 & 0.507 & 0.488 & 0.281 \\
47 & 0.524 & 0.559 & 0.524 & 0.505 & 0.297 \\
94 & 0.559 & 0.591 & 0.559 & 0.543 & 0.327 \\
188 & 0.588 & 0.625 & 0.588 & 0.572 & 0.349 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Segmentation Accuracy, Precision, Sensitivity, Dice Coefficient and IoU score for different numbers of sampled images from the target domain (Potsdam as source and Vaihingen as target). As illustrated in Tables 3 and 4, the algorithm efficiency is far better if we select Potsdam as the source. For similar number of labeled images (188 images in first case and 192 images in second case), improvement in average accuracy is 0.243 in first case and 0.026 in the second case. In fact, Potsdam dataset is far larger than Vaihingen dataset (3800 images in Potsdam and 459 images in Vaihingen). Training the segmentation model on a large dataset helps to better capture the global patterns of the data. Then, our algorithm will be more efficient in minimizing the domain shift that exists between the source and the target. This can be seen when selecting Potsdam as source in Table 3. If the source dataset is small, the segmentation model will be less able to learn the global patterns of the data. In this case, our algorithm will not be able to treat efficiently the domain shift. This can be seen when selecting Vaihingen as source a in Table 4. Hence, our algorithm works more efficiently if the source dataset is large enough to make the segmentation model capture the global patterns of the data. As shown in Table 3, without our algorithm, the total accuracy was 34.5%. Using only 1 labeled images, accuracy increases to 36.8%. The more labeled images are added, the more the accuracy increases until reaching 58.8% for 188 labeled images from the target domain. 188 images represents only 4.9% of the images that was needed to train the segmentation model on the source dataset (3800 images of size \(512\times 512\) which proves the data efficiency of our algorithm. Figures 12-16 respectively display the curves of accuracy, precision, sensitivity, F1 score and IoU for different numbers of sampled images from the target domain.