# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 1 Introduction
The algorithm autonomously classifies flood areas and estimates uncertainty values, demonstrating robustness and near-real-time operational suitability. It also contributed to the Global Flood Monitoring component of the Copernicus Emergency Management Service. Contextual filtering on multi-temporal SAR imagery resulted in an automated method for mapping non-urban flood extents [26]. Using tile-based histogram thresholding refined with post-processing filters, including multitemporal and contextual filters, the method achieved high accuracy. Additionally, confidence information was provided for each flood polygon, enabling stable and systematic inter-annual flood extent comparisons at gauged and ungauged sites. Finally, in [27], an unsupervised graph-based image segmentation method was proposed that aims to achieve user-defined and application-specific segmentation goals. This method utilizes a graph structure over the input image and employs a propagation algorithm to assign costs to pixels based on similarity and connectivity to reference seeds. Subsequently, a statistical model is estimated for each region, and the segmentation problem is formulated within a Bayesian framework using probabilistic Markov random field (MRF) modeling. Final segmentation is achieved through minimizing an energy function using graph cuts and the alpha-beta swap algorithm, resulting in segmentation based on the maximum a posteriori decision rule. In particular, the method does not rely on extensive prior knowledge and demonstrates robustness and versatility in experimental validation with different modalities, indicating its potential applicability across different domains. It was also successfully applied on SAR images for flood mapping. Our review of related literature reveals a prevailing preference for supervised methodologies in contemporary applications, as they are utilized more frequently than unsupervised approaches (see Table 1). Additionally, there is a preference for satellite radar imagery due to its greater availability. Among the unsupervised methods, we identified only one that processes RGB images. However, this method depends on change detection, necessitating the availability of pre-disaster images. In this paper, we propose a novel unsupervised method for flood segmentation utilizing color images acquired from UAVs. Without the need of large datasets, extensive labeling, augmentation, and training, the segmentation can be performed directly on the UAV deployed over the disaster area. Therefore, relief efforts can be swiftly directed to damaged sites, avoiding time loss, which can be crucial in saving lives and properties. Initially, we employ parameter-free calculated masks over each component of the LAB colorspace, utilizing as well an RGB vegetation index and the detected edges of the original image in order to provide an initial segmentation. Next, unsupervised image analysis techniques, such as distance transform, are adapted to the flood detection problem, producing a probability map for the location of flooded areas. Then, the hysteresis thresholding segmentation method is applied, resulting in the final segmentation. The main contributions of our work can be summarized as follows:  
* _Novelty in Approach:_ To our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs. Our work addresses flood segmentation using parameter-free calculated masks and unsupervised image analysis techniques without any need for training. * _Probability Optimization:_ Flood areas are identified as solutions to a probability optimization problem, with an isocontour evolution starting from high-confidence areas and gradually growing according to the hysteresis thresholding method. * _Robust Algorithm:_ The proposed formulation results in a robust, simple, and effective unsupervised algorithm for flood segmentation.