# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 4 Results and Discussion
### Ablation Study  
The ablation study outlines the significance of each of the proposed method's modules and is reported in Table 3. To do so, we have reported experiments of the proposed method variants, conducted with the Flood Area dataset. The proposed method (UFS-HT-REM) includes all modules and yields the best performance in the following metrics \(ACC=84.9\%\), \(PR=79.5\%\), \(F_{1}=79.1\%\), and \(\overline{F_{1}}=77.3\%\). First, we examine the simplification of the probability map (PM) estimation defined in Equation (8), including equally all the LAB color components (UFS-HT-REM Equation (9)) and the AB color components (UFS-HT-REM Equation (10)) for the decisive probability map computation. In both cases, the performance of the method is slightly degraded, since the reduction in \(F_{1}\) is less than 0.5%. This shows that the proposed Equation (8) is robust and can be replaced by a simpler formula without significant changes. Additionally, it shows that the initialization process is solid, leaving in the PFAs a majority of pixels that are truly flood. From the LAB color components, the luminance \(L\) is of greater importance, as proven by experiments exploiting only one color component:  
* \(L\) (UFS-HT-REM-L with \(F_{1}=74.8\%\))
* \(A\) (UFS-HT-REM-A with \(F_{1}=68.9\%\))
* \(B\) (UFS-HT-REM-B with \(F_{1}=70.8\%\))  
The core methodology, as described in Sections 3.2-3.4, performs quite adequately (UFS), with the resulting 4.6% lower \(F_{1}\) compared to UFS-HT-REM. Additionally, by adding the small area removal segment (UFS-REM) or the hysteresis thresholding technique (UFS-HT) separately, performance is improved by about 3% compared to the UFS. It implies that the hysteresis thresholding exerts a slightly better influence, resulting in 0.5% higher \(F_{1}\) compared to UFS-REM. Overall, each of the proposed components was carefully selected to enhance the final segmentation result. Finally, the proposed initialization of pixels as potential flood or non-flood using \(\Delta_{C}\) thresholding (see Equation (2)) is replaced by the Otsu thresholding [46] (UFS(Otsu)-HT-REM), resulting in 31.6% lower \(F_{1}\) compared to the UFS-HT-REM. This shows that exploiting the standard deviation of the color data relative to their central tendency of the LAB components is a crucial step in the proposed method. This is also proved by the evaluation of the \(M_{Final}\) Mask (see Equation (3)) without any other step of the proposed method, which yields \(F_{1}=72.7\%\), which is only 6.4% lower compared to the corresponding \(F_{1}\) of UFS-HT-REM. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & \(ACC\) & \(PR\) & \(REC\) & \(F_{1}\) & \(\overline{F_{1}}\) \\ \hline UFS-HT-REM & **84.9\%** & **79.5\%** & 78.6\% & **79.1\%** & **77.3\%** \\ \hline UFS-HT-REM (Equation (10)) & 84.7\% & 79.2\% & 78.5\% & 78.8\% & 77.0\% \\ \hline UFS-HT-REM (Equation (9)) & 84.2\% & 78.6\% & 78.7\% & 78.6\% & 76.7\% \\ \hline UFS-HT & 83.4\% & 76.3\% & 79.4\% & 77.8\% & 75.9\% \\ \hline UFS-REM & 82.5\% & 75.7\% & 79.1\% & 77.3\% & 74.9\% \\ \hline UFS-HT-REM—L & 79.9\% & 68.9\% & 82.0\% & 74.8\% & 72.8\% \\ \hline UFS & 78.6\% & 68.9\% & 81.1\% & 74.5\% & 71.6\% \\ \hline \(M_{Final}\) Mask & 76.0\% & 64.9\% & 82.7\% & 72.7\% & 69.8\% \\ \hline UFS-HT-REM—B & 74.6\% & 66.3\% & 76.1\% & 70.8\% & 67.8\% \\ \hline UFS-HT-REM—A & 68.6\% & 56.7\% & **88.0\%** & 68.9\% & 66.0\% \\ \hline UFS(Otsu)-HT-REM & 72.6\% & 66.9\% & 36.8\% & 47.5\% & 43.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study highlighting the contribution of the method’s modules. All experiments were conducted with the Flood Area dataset. To show the stability of the proposed system under different values of system parameters, we performed the following sensitivity test on the two parameters of the hysteresis thresholding technique.