# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 1 Introduction
Also, a CNN employed to automatically detect inundation extents using the Deep Earth Learning, Tools, and Analysis (DELTA) framework demonstrated high precision and recall for water segmentation despite a diverse training dataset. Finally, the effects of surface obstruction due to the inability of optical remote sensing data to observe floods under clouds or flooded vegetation are quantified, suggesting the integration of flood models to improve segmentation accuracy [20]. The efficacy of CNNs in semantically segmenting water bodies in highly detailed satellite and aerial images from various sensors, with a focus on flood emergency response applications, is assessed by combining different CNN architectures with encoder backbones to delineate inundated areas under diverse environmental conditions and data availability scenarios. A U-Net model with a MobileNet-V3 backbone pre-trained on ImageNet consistently performed the best in all scenarios tested, while the integration of additional spectral bands, slope information from digital elevation models, augmentation techniques during training, and the inclusion of noisy data from online sources further improved model performance [22]. U-Nets and their variations have been widely used to tackle the problems of water bodies segmentation and flood extent extraction. In [14], another adjusted U-Net was proposed. With carefully selected parameters and training with pre-processed Sentinel-l images for three-category classification, the proposed method was able to distinguish flood pixels from permanent water and background. Since rapid damage analysis and fast coordination of humanitarian response during extreme weather events are crucial, flood detection, building footprint detection, and road network extraction have been integrated into an inaugural remote sensing dataset called SpaceNet 8 and a homonym challenge has been launched. The provided satellite \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Year** & **Approach** & **Imagery** & **Method** \\ \hline [PERSON] et al. [23] & 2022 & Unsupervised & SAR & Datacube, time series-based detection, Bayes classifier \\ \hline [PERSON] et al. [24] & 2015 & Unsupervised & RGB (surveillance camera) & Change detection, color probability calculation \\ \hline [PERSON], [PERSON] et al. [25] & 2020 & Unsupervised & Sentinel-l, 2 & K-means clustering, region growing \\ \hline [PERSON], [PERSON] et al. [26] & 2022 & Unsupervised & Sentinel-l & Histogram thresholding, multi-temporal and contextual filters \\ \hline [PERSON], [PERSON] et al. [27] & 2023 & Unsupervised & SAR & Graph-based MRF segmentation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._imagery posed real-world challenges, such as varying resolutions, misalignment, cloud cover, and lighting conditions. Top performing DL approaches focusing on multi-class segmentation showed that swiftly identifying flooded infrastructures, such as buildings and roads, can significantly shorten response times. Simple U-Net architectures yielded the best balance of accuracy, robustness, and efficiency, with strategies such as pre-training and data augmentation proving crucial to improve model performance [8]. In Ref. [13], an improved efficient neural network architecture (ENet) was the choice to segment the UAV video of flood disaster. The proposed method consists of atrous separable convolution as the encoder and depth-wise separable convolution as the decoder. In Ref. [5], a multiscale attentive decoder-based network (ADNet) designed for automatic flood identification using Sentinel-1 images outperformed recent DL and threshold-based methods when validated on the Sen1f floods11 benchmark dataset. Through detailed experimentation on various dataset settings, ADNet demonstrated effective delineation of permanent water, flood water, and all water pixels using both co-polarization (VV) and cross-polarization (VH) inputs from Sentinel-1 images.