# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 4 Results and Discussions
### Results of Erbil Flood Detection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Metric**} \\ \cline{2-5}  & **OA** & **Kappa** & **F1 Score** & **mIoU** \\ \hline CNN 2D-1 Layer & 0.9041 & 0.8082 & 0.9031 & 0.8234 \\ \hline CNN 2D-2 Layers & 0.9056 & 0.8113 & 0.9044 & 0.8254 \\ \hline CNN 3D-1 Layer & 0.8741 & 0.7482 & 0.8642 & 0.7609 \\ \hline CNN 3D-2 Layers & 0.8892 & 0.7784 & 0.8845 & 0.7929 \\ \hline Hybrid CNN & 0.9008 & 0.8016 & 0.8985 & 0.8158 \\ \hline U-Net & 0.8953 & 0.7907 & 0.9013 & 0.8204 \\ \hline ViT & 0.9064 & 0.8127 & 0.9086 & 0.8326 \\ \hline CNN–Transformer & 0.9079 & 0.8158 & 0.9076 & 0.8308 \\ \hline CCT-U-ViT (ours) & **0.9124** & **0.8248** & **0.9121** & **0.8383** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy assessment of the proposed and benchmark models for flood detection on the Erbil dataset. Figure 9: Maps of the flood detection of the Erbil dataset using different deep-learning models (**a**) Pre-flood image; (**b**) Post-flood image; (**c**) Ground truth; (**d**) CNN 2D—1 layer; (**e**) CNN 2D—2 layer; (**f**) CNN 3D—1 layer; (**g**) CNN 3D—2 layer; (**h**) Hybrid CNN; (**i**) U-Net; (**j**) ViT; (**k**) CNN—Transformer; (**l**) CCT-U-Vit. The U-Net architecture demonstrates moderate performance and exhibits typical encoder-decoder artifacts. This includes occasional missed detections in smaller flooded areas and minor over-segmentation in transition zones. Such behavior is consistent with U-Net's optimization for biomedical image segmentation, where its skip connections, while preserving spatial resolution, may struggle with the spectral complexity and radiometric variations found in multispectral satellite imagery. The superior capabilities of attention-based models--especially in sustaining spatial coherence--highlight the advantages of explicitly modeling spatial relationships through attention mechanisms, compared to purely convolutional methods in complex Earth-observation tasks. The detailed error analysis presented in Figure 10 provides essential insights into the modes of model failure and their root physical and methodological causes. In addition, Figure 11 provides zoomed areas of the most significant differences among the prediction maps. The patterns of error distribution highlight consistent biases linked to key difficulties in satellite-based flood detection, such as spectral confusion, mixed pixel effects, and atmospheric interference. False positive detections, illustrated in yellow, mainly occur in regions with high soil moisture, shadows from topography, and permanent water bodies that closely resemble the spectral signatures of floodwaters. These errors are particularly evident in 3D CNN models, suggesting that including the temporal dimension, despite  
Figure 10: Error distributions of the flood detection for the proposed and baseline models. its theoretical benefits, may introduce noise when training data lacks adequate temporal diversity or when atmospheric conditions differ significantly between acquisition dates. False negative detections (shown in magenta) primarily occur in shallow flood zones and vegetated wetlands, where emergent vegetation or sediment load weakens the water signal. The lower false negative rates in transformer-based models are attributed to their superior capacity to capture contextual details and subtle spectral changes via global attention mechanisms. The CCT-U-ViT model exhibits the most balanced error distribution, featuring significantly reduced false positive rates (indicating enhanced precision) while maintaining high sensitivity for actual flood detection. This indicates that the hybrid architecture effectively integrates the spatial inductive biases of CNNs with the contextual modeling strengths of transformers.