# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors** & **Year** & **Approach** & **Imagery** & **Method** \\ \hline [PERSON] et al. [5] & 2023 & Supervised & Sentinel-1 & Multi-scale ADNet \\ \hline [PERSON] et al. [6] & 2022 & Supervised & Sentinel-1, 2 & CNN change detection \\ \hline [PERSON], [PERSON] et al. [7] & 2023 & Supervised & Sentinel-1 & STANets, SNUNet, BiT \\ \hline [PERSON] et al. [8] & 2022 & Supervised & HR satelite RGB & U-Net \\ \hline [PERSON] et al. [9] & 2024 & Weakly-supervised & HR aerial RGB & End-to-end WSSS framework structure constraints and self-distillation \\ \hline [PERSON] et al. [10] & 2021 & Supervised & UAV RGB & Optimized DNN \\ \hline [PERSON] et al. [11] & 2023 & Supervised & SAR & BCNN \\ \hline [PERSON] et al. [12] & 2021 & Semi-supervised & UAV RGB & RGB and HSI color models, k-means clustering, region growing \\ \hline [PERSON] et al. [13] & 2022 & Supervised & UAV video & Improved ENet \\ \hline [PERSON] et al. [14] & 2023 & Supervised & Sentinel-1 & U-Net \\ \hline [PERSON] et al. [15] & 2015 & Semi-supervised & RGB (Surveillance camera) & HSV color model, seeded region growing \\ \hline [PERSON] et al. [16] & 2021 & Supervised & UAV RGB & Landmark-based feature selection, CNN hybrid \\ \hline [PERSON] et al. [17] & 2023 & Supervised & HR satelite RGB & Swin transformer in a Siamese-UNet \\ \hline [PERSON] et al. [18] & 2021 & Supervised & UAV RGB & InceptionNetv3, ResNet50, XceptionNet, PSPNet, ENet, DeepLabv3+ \\ \hline [PERSON], [PERSON] et al. [19] & 2024 & Supervised & UAV RGB & ED network with EDR block and atrous convolutions (FASegNet) \\ \hline [PERSON], [PERSON] et al. [20] & 2023 & Supervised & WorldView 2, 3 multispectral & CNN with atrous convolutions \\ \hline [PERSON], [PERSON] et al. [21] & 2022 & Supervised & True Orthophoto (near infrared), DSM & Swin transformer and DCFAM \\ \hline [PERSON], [PERSON] et al. [22] & 2023 & Supervised & Satellite and aerial & U-Net model with MobileNet-V3 backbone pre-trained on ImageNet \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A brief overview of the research for this article, depicting approach (supervised, unsupervised), modality, and method. DL methods are increasingly applied to remote sensing imagery to address the limitations of traditional flood mapping techniques. Convolutional layer-based models offer improved accuracy in capturing spatial characteristics of flooding events, while fully connected layer-based models show promise when coupled with statistical approaches. Remote sensing analysis, multicriteria decision analysis, and numerical methods are replaced with DL models for flood mapping in which flood extent or flood inundation maps, susceptibility maps, and flood hazard maps determine, categorize, and characterize the disaster, respectively [28]. Furthermore, in a recent review, current DL approaches for flood forecasting and management are critically evaluated, highlighting their advantages and disadvantages. The challenges with data availability and potential future research directions are examined. The current state of DL applications in this area is fully evaluated, showing that they are a powerful tool to improve flood prediction and control [29]. Convolutional neural networks (CNNs) have proved to be effective in flood detection using satellite imagery. High-quality flood maps are generated with the help of temporal differences from various sensors after CNNs identify changes between permanent and flooded water areas using synthetic aperture radar (SAR) and multispectral images [6; 7]. In addition, Bayesian convolutional neural networks (BCNNs) have been recommended to quantify the uncertainties associated with SAR-based water segmentation, because of their greater flexibility to learn the mean and the spread of the parameter posterior [11].