# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 1 Introduction
To facilitate efficient processing of disaster images captured by UAVs, an AI-based pipeline was proposed enabling semantic segmentation with optimized deep neural networks (DNNs) for real-time flood area detection based directly on UAVs, minimizing infrastructure dependency and resource consumption of the network. The experimental results confirmed the feasibility of performing sophisticated real-time image processing on UAVs using GPU-based edge computing platforms [10]. It becomes clear that DL methods offer improved segmentation by creating adaptive mapping relationships based on contextual semantic information. However, these methods require extensive manual labeling of large datasets and lack interpretability, suggesting the need to address these limitations for further progress. Traditional ML methods, on the other hand, rely on manually designed mappings. Systematic reviews of water body segmentation over the past 30 years examine the application and optimization of DL methods and outline traditional methods at both pixel and image levels [33]. Evaluating the strengths and weaknesses of both approaches prompts a discussion of the importance of maintaining knowledge of classical computer vision techniques. There remains value in understanding and utilizing these older techniques. The knowledge gained from traditional computer vision (CV) methods can complement DL, expanding the available solutions. There also exist scenarios in which traditional CV techniques can outperform DL or be integrated into hybrid approaches for improved performance. Furthermore, traditional CV techniques have been shown to have benefits, such as reducing training time, processing, and data requirements compared to DL applications [34]. In 2015, a method for automatically monitoring flood events in specific areas was proposed using remote cyber-surveillance systems and image-processing techniques. When floods are treated as possible intrusion objects, the intrusion detection mode is utilized to detect and verify flood objects, enabling automatic and unattended flood risk level monitoring and urban inundation detection. Compared to large-area forecasting methods, this approach offered practical benefits, such as flexibility in location selection, no requirement for real-world scale conversion, and a wider field of view, facilitating more accurate and effective disaster warning actions in small areas [15]. Real-time methods to detect flash floods using stationary surveillance cameras, suitable for both rural and urban environments, have become quite popular. Another method used background subtraction to detect changes in the scene, followed by morphological closing to unite pixels belonging to the same objects. Additionally, small separate objects are removed, and the color probability is calculated for the foreground pixels, filtering out components with low probability values. The results are refined using the edge density and boundary roughness [24]. Unsupervised object-based clustering was also used for flood mapping in SAR images. The framework segments the region of interest into objects, converts them into a SAR optical feature space, and clusters them using K-means, with the resulting clusters classified based on centroids and refined by region growing. The results showed improved performance compared to pixel and object-based benchmarks, with additional SAR and optical features enhancing accuracy and post-processing refinement reducing sensitivity to parameter choice even in difficult cases, including areas with flooded vegetation [25]. The same techniques were also proposed for flood detection purposes in UAV-captured images. Employing RGB and HSI color models and two segmentation methods, K-means clustering and region growing, in a semi-supervised scheme, showed potential for accurate flood detection [12]. There is also a datacube-based flood mapping algorithm that uses Sentinel-1 data repetition and predefined probability parameters for flood and non-flood conditions [23].