# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 4 Results and Discussion
### Flood Semantic Segmentation Dataset: Experimental Results and Discussion  
To demonstrate the generalization of the proposed algorithm, we used the second dataset, as described in Section 2. It comprises more than twice the number of images compared to the initial one. All images were acquired in diverse real-world scenarios and under different conditions. Without any image pre-processing and modification of the code or parameters, the algorithm performed even higher, reaching 88.5% and 81.7% in accuracy and F1-score, respectively. Consequently, we encountered an increase of 3.6% (2.6%) in _ACC_ (\(F_{1}\)). This demonstrates that the observations on which our method is based are universally applicable and resilient. Moreover, it proves that when controllable variables, like the camera's rotation angle, are considered, segmentation outcomes exhibit enhancement, an observation we found to be consistent within this dataset due to the reduced occurrence of sky portions in images. Quantitative metrics in comparison with the first dataset are presented in Table 5. Also, we provide the weighted average in the last row to give an overview of the overall performance of the algorithm in segmenting the flood for 953 images, which depict various scenes and were acquired with different camera settings, e.g., camera rotation angle, focal length, etc.  
Representative results for the Flood Semantic Segmentation dataset are shown in Figure 13. Flood segmentation is shown in blue overlaying the original image with the flood's borders emphasized in dark blue. The same technique is used to present the ground truth as well. The best flood segmentation scored 99.6% in \(F_{1}\). As we can observe, the excellent segmentations are capable of capturing the flood in its full or almost full extent (Figure 13a,b). Furthermore, high-performing results segment flood details in challenging environments characterized by interference of numerous natural or man-made obstacles (Figure 13c,d). Of course, the same issues leading to poor performance exist, as described in Section 4.3. Figure 13e presents such a low-performance segmentation, where the luminosity of the sky prevents its exclusion from the PFAs. This results in the sky's pixels being assigned significant weights, consequently influencing subsequent probabilities, and ultimately causing them to be misclassified as flood during the hysteresis thresholding isocontour evolution procedure.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & _ACC_ & _PR_ & _REC_ & \(F_{1}\) & **Tr. Par.** \\ \hline FASegNet & **91.5\%** & **91.4\%** & 90.3\% & **90.9\%** & 0.64 M \\ \hline UNet & 90.7\% & 90.0\% & 90.1\% & 90.0\% & 31.05 M \\ \hline HRNet & 88.6\% & 84.8\% & **92.0\%** & 88.3\% & 28.60 M \\ \hline Ours & 84.9\% & 79.5\% & 78.6\% & 79.1\% & **0 M** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of our proposed approach with selected DL approaches on the Flood Area dataset. The metrics used are accuracy (_ACC_), precision (_PR_), recall (_REC_), and F1-score (\(F_{1}\)) (calculated as in Equation (14)) expressed in percentage, and trainable parameters (Tr. Par.) expressed in millions (M).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dataset** & **Images** & _ACC_ & _PR_ & _REC_ & \(F_{1}\) & \(\overline{F_{1}}\) \\ \hline FSSD & 663 & **88.5\%** & **79.8\%** & **83.7\%** & **81.7\%** & **79.4\%** \\ \hline FAD & 290 & 84.9\% & 79.5\% & 78.6\% & 79.1\% & 77.3\% \\ \hline FSSD \(\cup\) FAD & 953 & 87.4\% & 79.7\% & 82.2\% & 80.9\% & 78.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative findings for the Flood Semantic Segmentation dataset (FSSD) in comparison with the Flood Area dataset (FAD) and for the union of the two datasets (calculated as the weighted average due to the varying number of images). The number of images for each dataset (_Images_), accuracy (_ACC_), precision (_PR_), recall (_REC_), and F1-score (\(F_{1}\)) (calculated as in Equation (14)) expressed in percentage are reported.