# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Experimental Results
#### 3.3.1 Experimental Results on GID Dataset  
The specific performance indicators of each model on the GID dataset are shown in Table 2. The bold data in Table 2 represents the best parameters among all models. The results show that DMLU-Net performed outstandingly in terms of OA, P, R, F1-score, and IoU and that it was significantly superior to the other methods. Specifically, compared with the closest model MU-Net, DMLU-Net achieved a 0.52% improvement in the IoU and a 0.86% improvement in the F1-score; compared with QTU-Net, it achieved a 2.15% improvement in the IoU. The experimental findings validate the model's superior accuracy and robustness in complex water body extraction, particularly for small water bodies and fragmented boundaries. To intuitively reflect the performance differences of the models, Figure 6 visually displays the prediction results of DMLU-Net and the other segmentation networks on partial remote sensing images. The first column in Figure 6 shows the original images, while the other columns show the extraction results of each model. The black area in the results represents the non-water body areas detected by the model, and the white area represents the water body areas. The parts with obvious differences are circled and framed with red dashed lines. From the extraction results of various water bodies in the figure, it can be seen that our method generally performs better than the other six methods in different scenarios. The extraction results of each model for surface water bodies in the fourth row of Figure 6 highlight the superiority of DMLU-Net. Its extraction results have \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **OA** & **P** & **R** & **F1** & **IoU** \\ \hline Unet & 95.70 & 95.81 & 90.84 & 93.51 & 87.52 \\ Deeplabv3+ & 95.05 & 92.06 & 93.24 & 93.08 & 86.54 \\ TransUNet & 95.57 & 91.57 & 92.14 & 92.82 & 86.53 \\ SwinUnet & 94.22 & 91.64 & 90.02 & 91.29 & 84.57 \\ MU-Net & 95.72 & 94.48 & 94.88 & 93.64 & 89.94 \\ QTU-Net & 95.87 & 93.61 & 94.56 & 93.85 & 88.31 \\ DMLU-Net & **96.21** & **94.29** & **95.19** & **94.50** & **90.46** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results based on the GID dataset. clear water body boundary contours, with the least classification errors and missed small water bodies, and they are closest to the true ground labels. Specifically, Deeplabv3+ tends to exhibit over-segmentation in scenes of meandering and narrow tributaries, misclassifying the surrounding areas as water bodies. TransUnet, however, suffers from detection failures when identifying small tributaries with complex branch structures, primarily due to its inability to learn abundant global contextual features. Across diverse scenarios, although competing segmentation networks recognize water body fluctuations, DMLU-Net delivers more consistent and accurate segmentation outcomes. Moreover, in the scene of large-scale water bodies, the boundaries of DMLU-Net are the clearest. This indicates that DMLU-Net can effectively capture local details and global contextual information through the DMLKA module. The large-kernel convolution design in the DMLKA module enables the model to better perceive long-range dependencies, thus improving its efficiency. Relative to other network architectures, DMLU-Net excels in capturing sharper and more accurate water body boundaries, which is attributed to its multi-scale feature fusion mechanism. To further validate the robustness and generalization capability of DMLU-Net, we conducted comparative experiments on the LoveDA dataset. The experimental results are presented in Table 3. The bold data in Table 3 represents the best parameters among all models. On this dataset, DMLU-Net consistently outperformed the other water body recognition networks based on CNNs and Transformers across all evaluation metrics. Here,  
Figure 6: Partial extraction results of each model on the GID dataset.