# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 4 Results and Discussion
### Flood Area Dataset: Experimental Results and Discussion  
We showcase a series of final segmentations as a result of our proposed approach (UFS-HT-REM) supplemented by evaluation metrics, as described above, and the original and ground-truth images. All images have been adjusted to uniform dimensions (800 \(\times\) 600) for illustration purposes only, since our method works for any image size.  
Representative outcomes from the Flood Area dataset are shown in Figures 9-11. The results show flood segmentation in blue overlaid on the original image with the flood's borders emphasized in dark blue. We also use the same technique to represent the ground truth. In Figure 9, the results of the proposed method yield \(F_{1}\) and \(ACC\) that exceed 90%, showing high-performance results with almost perfect flood detection. In Figure 10, \(F_{1}\) belongs to the range [75%, 90%], providing results where the flood area detection accuracy is satisfactory. In Figure 11, the results of the proposed method yield \(F_{1}\) lower than 30%, showing poor segmentation results.  
The proposed unsupervised approach works for any tint of flood water and delivers excellent results even when a plethora of small objects protrude the flood, managing to segment around them (see Figures 9b and 10d). As we can observe in the rest of the results, it achieves satisfactory results in urban/peri-urban as well as rural environments, where it accurately segments existing flooded areas, even when they are not labeled in the ground truth, as shown in Figure 10e. The method works best when the acquired image's color quality and the weather and lighting conditions are good, alongside a low camera rotation angle (top-down view). Naturally, should the assumptions underpinning the proposed algorithm not hold, it may result in suboptimal outcomes (see Figure 11). Reasons for bad results include extreme similarity of the flood and the background color, and elevated LAB color components' values of background areas. It is essential that the flood is not green in color, because the vegetation index will confuse it with greenery and, therefore, mainly exclude it (see Figure 11c).  
In the LAB colorspace, the flood has been measured to have elevated values with respect to the background. Therefore, in cases when light is reflected on the surface or the sky is of the same brightness, it is impossible for the method to set these two areas apart (see Figure 11a,b). Finally, because the method still relies on color, if there is near-identical coloration of the flood water and other objects (e.g., buildings, rooftops), these objects will be considered part of the flood or will be entirely mistaken as flood, omitting the true flood water (see Figure 11d). High \(ACC\) scores in the poor segmentation results are due to correctly segmenting large areas of the background.  
Figure 9: High-performance results of the proposed flood segmentation method from the Flood Area dataset. Original images, ground truth, and the final segmentation of our proposed method (UFS-HT-REM).  
Figure 10: Satisfactory results of the proposed flood segmentation method from the Flood Area dataset. Original images, ground truth, and our proposed method’s (UFS-HT-REM) final segmentation.