# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 5 Conclusions and Future Work  
Overall, we presented a fully unsupervised approach for flood detection in color images acquired by flying vehicles such as UAVs and helicopters. The method progressively eliminates image regions identified as non-flood using binary masks generated from color  
Figure 13: Representative results of the proposed methodology (UFS-HT-REM) from the Flood Semantic Segmentation dataset. Original images, ground truth, and the final segmentation of the proposed method are shown from left to right. and edge data. Our method operates in a fully unsupervised manner, with no need for training and ground truth labeling. We iteratively apply the same algorithmic steps to each color component. Subsequently, a binary map is generated for each component, discarding regions identified as non-flood and producing a final mask of potential flood areas (PFAs), refined through basic morphological operations. By weighting the pixels within the PFAs, we calculate an estimation of the dominant color of the flood, and a hysteresis thresholding technique is employed to achieve the final segmentation through probabilistic region growing of an isocontour. To the best of our knowledge, it is the first unsupervised approach to tackle this problem. In this work, we showed that the following simple features suffice to accurately solve the problem of unsupervised flood detection. First of all, the flood's color is similar wherever it appears within the image, and this color differs from the background. Almost always, the flood's color is not green, assuming tree-like vegetation to be covered with water is extreme. Finally, in the LAB colorspace, the flooded area exhibits a higher value in at least one of the color components than the background. The color quality and camera rotation angle of the captured image contribute to the solidity of our observations, and thus a good amount of control over the flying vehicle while capturing the images will support the aforementioned inferences. To show the robustness of the proposed method, we utilized two datasets containing a total of 953 images, representing diverse real-world areas affected by flood events. All images were acquired by various UAVs operating under different weather conditions and performing a range of flying maneuvers over diverse areas. Our method demonstrated strong generalization to new, unseen data, as it is entirely unsupervised and parameter-free. Since no training is required, all images were exclusively used for testing purposes. Furthermore, we have introduced a categorization of the Flood Area dataset, according to the depicted scenery and camera rotation angle, into rural and urban/per-urban, and no sky and with sky, respectively. We showed that our approach performs well in all categories, slightly excelling in segmenting floods in rural environments and is better suited for acquired images that do not contain sky, which is a controllable factor when maneuvering the UAV. Experimental results confirmed that our proposed approach is robust, performs well in metrics, and is comparable to recent DL approaches, although not outperforming them. The proposed parameter-free approach is highly efficient, with an inference time of approximately half a second, and does not require image pre-processing or GPU core processing capabilities. This makes the method suitable for on-board execution, allowing real-time flood segmentation to guide relief efforts, thereby preventing loss of life and mitigating the impact of floods on infrastructure. Although the proposed unsupervised method does not outperform any supervised method, it scores in close proximity, being lower by only 6.6% of the best DL method (FASegNet) in accuracy. In future research, we plan to extend this work by detecting flooded buildings and roads. This will refine existing flood segmentations and correct erroneous segmentations that occur when the observations on which the method relies do not apply to the image. Combined with suitable methodologies which identify buildings and roads, such as Ref.