## 5 Results and Discussions
performance, being the best performing backbones of homogenous subsets based on post-hoc tests. The worst performer is VGG16, one of the most used CNN for classification tasks (as seen in Figure 4, it obtained the lowest average IoU and F1 scores and the highest average loss). When applying [PERSON]'s HSD test to the performance metrics grouped by the base architectures, FPN is the worst performer (with average IoU and F1 scores of 0.5388 and 0.6757, respectively, and an average loss of 0.5447). This may be due to the FPN architecture, where a tile is scaled at different sizes, each scaled result being processed individually to provide the predictions. U-Net is the best performer (average IoU and F1 scores of 0.5465 and 0.6820, respectively, and an average loss of 0.5380), the results being significantly different when compared to FPN. However, LinkNet (with an average IoU score of 0.5422, an average F1 score of 0.6789 and an average loss of 0.5413) is not significantly different from FPN or U-Net and we do not see any significant difference in their performance when \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Config. No.**} & \multicolumn{5}{c}{**Homogeneous Subsets**} \\ \cline{2-6}  & **1** & **2** & **3** & **4** & **5** \\ \hline
13 & 0.5276 & & & & \\
3 & 0.5285 & & & & \\
8 & 0.5293 & & & & \\
11 & 0.5298 & & & & \\
6 & 0.5302 & & & & \\
1 & 0.5335 & 0.5335 & & & \\
9 & 0.5372 & 0.5372 & & & \\
14 & 0.5421 & 0.5421 & 0.5421 & & \\
12 & 0.5437 & 0.5437 & 0.5437 & 0.5437 & \\
4 & 0.5466 & 0.5466 & 0.5466 & 0.5466 & 0.5466 \\
15 & & 0.5509 & 0.5509 & 0.5509 & 0.5509 \\
10 & & 0.5515 & 0.5515 & 0.5515 & 0.5515 \\
2 & & & 0.5593 & 0.5593 & 0.5593 \\
7 & & & & 0.5626 & 0.5626 \\
5 & & & & & 0.5644 \\ \hline Sig. & 0.055 & 0.092 & 0.127 & 0.059 & 0.098 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Homogeneous subsets of configurations with post-hoc tests for the IoU score. Figure 4: Boxplots of performance metrics for different backbones. base architectures are considered alone. Figure 5 presents the average IoU scores, F1 scores and the losses obtained by the base architectures. To study the effect of backbones and the base architectures, we use two-factor analysis of variance models, and the results are presented in Table 3.  
The main effects of backbones and base architectures on performance are significant at a level of significance of 5%. The main effect null hypothesis studies the marginal effect of a factor when all other factors are kept at a fixed level and states that the effect is not significant. In Table 3, we can see that the effect of backbones on performance metrics when all other factors are kept at a fixed level is significant on the performance metrics and we can reject the null hypothesis and conclude that the effect is significant (\(p\)-value of 0.000; smaller than 0.05). Thus, there is a difference in performance due to different backbones. Similarly, there is a significant difference in performances due to different base architectures (\(p\)-values of 0.01 and 0.02, respectively). For the interaction effect, the null hypothesis is that the effect of backbones on the performance does not vary with the base architecture. The interaction effects of backbone and base architectures on IoU and F1 scores are not significant with \(p\)-values 0.073 and 0.111, respectively. So, we can conclude that there is not enough evidence to suggest that the effects of backbones are different for different base architectures. The null hypothesis is not rejected, the \(p\)-values being greater than 0.05. When trained on the full dataset, the best performing network was the hybrid configuration No. 5 (with U-Net [10] as segmentation architecture and SEResNeXt50 [18] as backbone), obtaining an IoU score of 0.5788, a F1 score of 0.7120, and a loss of 0.4985. These performance scores were computed from the confusion matrix obtained by evaluating the test set (presented in Figure 6 with a support of 1550 tiles, or 101,580,800 pixels).