## 4 Experiments
### Experimental Results  
The experiments were conducted on two distinct datasets, and the selection of comparative experiments was divided into two categories. In the initial experiment, we utilize the scene classification dataset introduced in Section 2 of this paper. Firstly, we employ well-established and widely used models based on relevant research articles pertaining to semantic segmentation. These models were chosen not only as comparative benchmarks for our lake classification task but also due to their frequent appearance in other studies related to semantic segmentation, such as U-Net, FCN8, PSP-Net, and DeepLabV3+. Secondly, as our model primarily focuses on the construction of the U-Net network, we incorporated the latest lightweight attention module in semantic segmentation to compare experimental results. These include Attention-U-Net, as well as U-Net with attention modules such as CBAM (convolutional block attention module), SE (squeeze-and-excitation) module, and CA (coordinated attention) module. Additionally, During the training of the initial five models in the enumerated list, pre-training weights were employed for transfer learning. The final experimental results are presented in Table 1, demonstrating that our designed GEA-MSNet model outperforms other models tested in this study across various indicators: mIoU (75.49%), recall (83.79%), PA (90.21%), and F1-score (83.25%). These findings suggest that the GEA-MSNet model is exceptionally effective for lake segmentation tasks. In addition, although the number of parameters increased compared with U-Net, FCN network and U-Net adding different lightweight attention modules, but the segmentation effect produced by these models was general. Compared with DeepLabV3+ and PSP-Net models with a good segmentation effect, the number of parameters in GEA-MSNet was not only smaller than the above models but also produced a better segmentation effect. Overall, the model improved the accuracy by approximately 5% compared with other methods without too much increase in the number of parameters, which was due to the design of depthwise separable convolution and global normalization in the efficient attention module GEA, which had less parameters while fusing the spatial information and global information. The visualization effects of different models on the lake scene classification datasets are presented in Figure 8. In this study, a subset of images containing small water bodies and complex edges from the datasets were selected for comparison to evaluate the performance of various models. Furthermore, it is observed that U-Net, FCN8, PSP-Net, and Attention-UNet models exhibit relatively low overall accuracy with indistinct segmentation boundaries in the target area. Some small water bodies are either missing or misclassified. However, despite the incorporation of various attention modules (SE, CBAM, Los Angeles, CA, USA) into U-Net to enhance the accuracy of water body edge extraction, the issue of indistinct boundaries still persists. Finally, we propose that the GEA-MSNet model effectively refines the edge features and accurately extracts micro water bodies while effectively delineating the transitional zone between land and lake surfaces. This approach mitigates misclassification caused by variations in water color and enhances the overall precision in lake extraction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **mIoU (\%)** & **Recall (\%)** & **PA (\%)** & **F1-Score (\%)** & **Parameters (M)** \\ \hline U-Net & 63.05 & 75.07 & 83.55 & 76.63 & 34.53 \\ FCN8 & 61.35 & 72.57 & 83.02 & 73.16 & 30.12 \\ PSP-Net & 64.86 & 76.57 & 84.51 & 76.96 & 49.07 \\ DeepLabV3+ & 66.24 & 77.27 & 85.28 & 77.89 & 54.71 \\ Attention-U-Net & 63.50 & 75.66 & 83.65 & 75.95 & 37.19 \\ CBAM-U-Net & 63.13 & 74.26 & 84.02 & 76.27 & 34.57 \\ SE-U-Net & 65.28 & 76.61 & 84.89 & 77.59 & 34.57 \\ CA-U-Net & 66.17 & 77.84 & 85.15 & 77.69 & 34.56 \\ \hline \hline \end{tabular}
\end{table}