# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 4 Results and Discussion
### Exploring the Impact of Environmental Zones and Camera Rotation Angle  
Additionally, we study how the environmental zone and the camera rotation angle affect the flooding segmentation by splitting the Flood Area dataset into the following categories:  
1. Environmental zone: 1. Rural, predominantly featuring fields, hills, rugged mountainsides, scattered housing structures reminiscent of villages or rural settlements, and sparse roads depicted within the images. It consists of 87 out of the 290 images in the dataset. 2. Urban and peri-urban, distinctly showcasing urban landscapes characterized by well-defined infrastructure, that conforms to urban planning guidelines; a dense network of roads and high population density reflected in the pres  
Figure 11: Poor segmentations resulting from the proposed methodology (UFS-HT-REM) from the Flood Area dataset. Original images, ground truth, and the final segmentation of our proposed method. ence of numerous buildings and structures. It encompasses a collection of 203 images. 2. Camera rotation angle: 1. No sky (almost top-down view, low camera rotation angle), distinguished by the absence of any sky elements; specifically, these images entirely lack any portion of the sky or clouding within their composition. It comprises 182 images of the dataset. 2. With sky (bird's-eye view, high camera rotation angle), where elements of the sky, such as clouds or open sky expanses, are visibly present within the image composition. It encompasses the remaining 103 images within the dataset. This categorization has been undertaken to emphasize that the environment in which the flood is situated can play a role in the final outcome and to encourage further studies to distinguish in their methodologies urban and rural floods, due to their different characteristics. As we can observe, in the evaluation metrics for these categories, shown in Table 2, our method performs well in all categories. Slightly better results are achieved when the scenery depicts a rural landscape, with \(F_{1}=80.3\%\) being 1.8% higher than in the urban/peri-urban category. The extensive greenery in rural areas allows for the exclusion of a significant number of pixels from PFAs using the RGBVI mask, as described in Section 3.2. However, in the absence of vegetation, the RGBVI does not impact the method's performance, as there will be no greenery to detect. In addition, flooded areas in such environments are usually large in size, which strengthens the dominant color estimation procedure (see Section 3.4), because plentiful pixels are engaged (see category 1. (a) in Table 2). In urbanized areas, the presence of rooftops, buildings and cars, which can have a similar color to flood, leads to mildly decreased performance with \(F_{1}=78.5\%\), given that parts of these objects are misclassified as flood by the algorithm (see category 1. (b) Table 2). Furthermore, as we have stated previously, poor segmentation results have been generated when the image featured the sky or parts of the sky. Since the sky's LAB color component values can fall above \(\Delta_{\text{C}}\) (as in Equation (2)), and according to our observation that the flood exhibits higher values in the LAB colorspace, parts of the sky are not excluded from the PFAs in this case. Their pixels are involved in the dominant color estimation and they are commonly segmented as flood. In Table 2, rows 2. (a) and 2. (b) present the evaluation metrics for the categories 'no sky' and 'with sky', with the F1-scores being 79.8% and 77.7%, respectively, in which clearly the 'no sky' group prevails by 2.1%, as expected. This categorization leads to the deduction that when the rotation angle of the UAV's camera is minded, so that the acquired image does not depict any part of the sky but is facing only terrain (a fact that can be controlled by the human operator or the navigation software), the segmentation result is improved.