# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 3 Results and Discussion
### Ablation Study  
To verify the effectiveness of the combination of SE block and CBAM in the ResNet50 network, we conduct a series of ablation experiments on the test set, and the results of the experiments are shown in Table 3.  
From Table 3, the following results can be obtained: with the addition of different modules, the model is improved in all three performance evaluation metrics, _PA_, _MPA_, and _MloU_, which effectively proves the role of each module and its combination on the structure \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Environment** & **Platform** & **Configuration** \\ \hline Hardware & CPU & Intel(R) Xeon(R) Gold 6240 CPU @ \\  & Memory & 32 G \\  & GPU & NVIDIA Tesla V100 32 GB \\ Software & Operating system & Linux Centos 7.6 \\  & Programming language & Python 3.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental environment configuration. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{3}{c}{**Evaluation Metric**} & \multirow{2}{*}{**Training Time per Epoch (s)**} \\ \cline{2-3}  & **PA** & **MPA** & **MIoU** & \\ \hline ResNet18 & 98.5 & 98.55 & 96.9 & 28 \\ ResNet34 & 98.6 & 98.65 & 97.1 & 80 \\ ResNet50 & 98.8 & 98.85 & 97.6 & 82 \\ ResNet101 & 98.8 & 98.85 & 97.6 & 95 \\ ResNet152 & 98.8 & 98.85 & 97.6 & 114 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ResNet backbone selection test results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{3}{c}{**Evaluation Metric**} & \multirow{2}{*}{**Training Time per Epoch (s)**} \\ \cline{2-3}  & & **PA** & **MPA** & **MIoU** & \\ \hline ResNet18 & 98.5 & 98.55 & 96.9 & 28 \\ ResNet34 & 98.6 & 98.65 & 97.1 & 80 \\ ResNet50 & 98.8 & 98.85 & 97.6 & 82 \\ ResNet101 & 98.8 & 98.85 & 97.6 & 95 \\ ResNet152 & 98.8 & 98.85 & 97.6 & 114 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ResNet backbone selection test results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**SE**} & \multirow{2}{*}{**CBAM**} & \multicolumn{3}{c}{**Evaluation Metric**} & \multirow{2}{*}{**Training Time per Epoch (s)**} \\ \cline{3-4}  & & **PA** & **MPA** & **MIoU** & \\ \hline \(-\) & \(-\) & 97.9 & 97.85 & 95.8 & 77 \\ \(+\) & \(-\) & 98.1 & 98.2 & 96.2 & 81 \\ \(-\) & \(+\) & 98.3 & 98.45 & 96.6 & 78 \\ \(+\) & \(+\) & 98.8 & 98.85 & 97.6 & 82 \\ \hline \hline \end{tabular} Note: the \(+\) mark indicates that the corresponding module is retained, and the \(-\) mark indicates that the corresponding module is removed.  
\end{table} 
Table 3: Results of ablation experiments. of the ResNet50 network. Compared to ResNet50, on the three performance evaluation metrics, the addition of SE block improves 0.2%, 0.35%, and 0.4%; the addition of CBAM improves 0.4%, 0.6%, and 0.8%; and the combination of the two modules improves 0.9%, 1%, and 0.88%, with a runtime increase of only 5 s, so the full architecture achieves optimal model performance, respectively. The removal of either module causes a decrease in model evaluation performance. Based on the above experimental results, we propose the R50A3-LWBENet model for semantic segmentation of remote sensing images of lake water bodies (Figure 6). As can be seen in Figure 6, the raw image with input size of (\(256\times 256\times 3\)) is first reshaped to (\(128\times 128\times 64\)) size by the convolutional layer with convolutional kernel size of (\(7\times 7\times 64\)), then cropped to (64 \(\times\) 64 \(\times\) 64) size by the MaxPool layer, and then compressed to (\(8\times 8\times 2048\)) size by the four down-sampling layers(The first layer has 3 residual modules with a total of 9 convolutional and 3 SENet attention layers; the second layer has 4 residual modules with a total of 12 convolutional and 4 SENet attention layers; the third layer has 6 residual modules with a total of 18 convolutional and 6 SENet attention layers; and the fourth layer has 3 residual modules with a total of 9 convolutional and 3 SENet attention layers).