## 4 Experiments
### Experimental Results

Table 1: Experimental results of different models in the lake scene classification datasets. Figure 8: Visualization results showing different models on a lake scene classification datasets: (**A**) image; (**B**) basic fact; (**C**) U-Net results; (**D**) FCN8 results; (**E**) PSP-Net results; (**F**) DeepLabV3+  
results; (**G**) Attention-UNet results; (**H**) CBAM-UNet results; (**I**) SE-UNet results; (**J**) CA-UNet results; and (**K**) GEA-MSNet results. The black area in the prediction results is the background, and the red area is the target water body. The white circle area represents the focus area where the prediction occurs. Furthermore, to ensure the model's generalization performance, the generalization of the model was tested using the lake dataset from the Tibetan Plateau. For this test, we selected certain models from the initial experiment and conducted additional comparative experiments by including both classical models and attention models with higher accuracy identified in Experiment 1. Although the datasets exhibit a high overall image brightness, which may lead to confusion between clouds and lakes, their annotations are accurate, making them an ideal choice for evaluating the segmentation performance of our model. The experimental data demonstrate that our model performance remains optimal. However, owing to the extensive dataset and precise annotations incorporated in this dataset, the experiments exhibit an elevated level of segmentation accuracy. The segmentation outcomes for the Tibetan Plateau lake datasets are presented in Table 2.  
According to Table 2, we conducted a comparative analysis of the evaluation metrics for different network architectures using the Qinghai-Tibet Plateau lake datasets. It is noteworthy that the GEA-MSNet model exhibited a superior semantic segmentation accuracy on these datasets, with an increase of 0.24% in pixel accuracy compared to the top-performing DeepLabV3+ model, as well as improvements of 0.46% in mIoU and 0.2% in Recall. According to the experimental results, even if other remote sensing lake segmentation datasets were replaced, our model also achieved the best segmentation accuracy, and the model has good generalization performance and visualization results can be observed in Figure 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mIoU (\%)** & **Recall (\%)** & **PA (\%)** & **F1-Score (\%)** \\ \hline U-net & 95.51 & 97.67 & 97.79 & 96.76 \\ DeepLabV3+ & 96.79 & 98.40 & 98.42 & 97.64 \\ CBAM-U-Net & 95.57 & 97.65 & 97.83 & 97.40 \\ GEA-MSNet & 97.57 & 98.86 & 98.82 & 98.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The experimental results of various models applied to the datasets of lakes in the Tibetan Plateau. Figure 9: Displaying different models on Qinghai–Tibet Plateau lake datasets: (**A**) image; (**B**) ground true; (**C**) U-net; (**D**) DeepLabV3+; (**E**) CBAM-UNet; and (**F**) GEA-MSNet. The black area in the prediction results is the background, and the red area is the target water body. The white circle area represents the focal area where the prediction occurs. For the visualization results in Figure 9, we specifically selected the DeepLabV3+ model and CBAM-UNet model. Regarding the lake edge segmentation, our model demonstrates a remarkable segmentation effect with significantly improved edge blurring compared to other models. In terms of small lake extraction, although our model does not achieve the complete extraction of all small water targets, its overall performance surpasses that of other models. Consequently, our model achieves commendable outcomes in the task of water segmentation.