## 3 Results
### Comparison of Models
#### 3.1.1 Quantitative Evaluation  
In Tables 3-5 the mean accuracy, loss, F1 Score, and IOU are shown for each of the compared models during training, validation, and testing, respectively, with Sentinel-1 images being employed. These results show that the proposed model was able to consistently outperform each of the state-of-the-art models, showcasing its effectiveness in handling Sentinel-1 imagery, and its superior robustness and ability to generalize to unseen data, making it the most suitable for practical flood mapping applications with Sentinel-1 imagery.  
Tables 6-8 show the mean accuracy, loss, F1 Score, and IOU during the training, validation, and testing of the models trained with Sentinel-2 images, where the superiority of the proposed model was further emphasized. Of the state-of-the-art models, U-Net++ with ResNet50 was the best performing with Sentinel-1 images; however, with Sentinel-2 images U-Net++ with MobileNet_V2 exhibits superior performance, suggesting that  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **F1 Score** & **IOU** \\ \hline U-Net++ ResNet50 & 0.9301 & 0.2086 & 0.6921 & 0.5422 \\ U-Net++ MobileNet\_V2 & 0.9185 & 0.2213 & 0.6537 & 0.5234 \\ DeepLabV3+ ResNet50 & 0.9196 & 0.2146 & 0.6448 & 0.5207 \\ DeepLabV3+ MobileNet\_V2 & 0.9205 & 0.2093 & 0.6829 & 0.5401 \\ MA-Net ResNet50 & 0.9201 & 0.2431 & 0.6351 & 0.4819 \\ MA-Net MobileNet\_V2 & 0.9101 & 0.2733 & 0.6056 & 0.4723 \\ Proposed & 0.9436 & 0.1724 & 0.7376 & 0.5908 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Validation scores with Sentinel-1 images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **F1 Score** & **IOU** \\ \hline U-Net++ ResNet50 & 0.9387 & 0.1763 & 0.7098 & 0.5509 \\ U-Net++ MobileNet\_V2 & 0.9242 & 0.2108 & 0.6623 & 0.5345 \\ DeepLabV3+ ResNet50 & 0.9237 & 0.1882 & 0.6502 & 0.5272 \\ DeepLabV3+ MobileNet\_V2 & 0.9389 & 0.1784 & 0.6962 & 0.5491 \\ MA-Net ResNet50 & 0.9206 & 0.2349 & 0.6565 & 0.5201 \\ MA-Net MobileNet\_V2 & 0.9398 & 0.2249 & 0.6739 & 0.5425 \\ Proposed & 0.9478 & 0.1342 & 0.7425 & 0.5997 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training scores with Sentinel-1 images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **F1 Score** & **IOU** \\ \hline U-Net++ ResNet50 & 0.9298 & 0.2743 & 0.6764 & 0.5404 \\ U-Net++ MobileNet\_V2 & 0.9127 & 0.2884 & 0.6509 & 0.5218 \\ DeepLabV3+ ResNet50 & 0.9124 & 0.2789 & 0.6249 & 0.5197 \\ DeepLabV3+ MobileNet\_V2 & 0.9196 & 0.2752 & 0.6789 & 0.5388 \\ MA-Net ResNet50 & 0.9078 & 0.2825 & 0.6037 & 0.4727 \\ MA-Net MobileNet\_V2 & 0.9008 & 0.2852 & 0.5956 & 0.4590 \\ Proposed & 0.9432 & 0.2237 & 0.7176 & 0.5862 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Testing scores with Sentinel-1 images.  
each state-of-the-art model is better suited to a particular data type. The proposed model, however, outperformed each of the state-of-the-art models across both image types. This demonstrates its ability to adapt to both SAR and optical image sources, further reinforcing its suitability for practical applications, as it will provide consistently high performance regardless of available data.