# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 4 Conclusions  
To address the challenges of large-scale variations, indistinct boundaries, and background interference in water body extraction from remote sensing imagery, in this study, we propose DMLU-Net, a novel hybrid neural network architecture. Building upon the encoder-decoder framework of U-Net, the model incorporates three key components: a dynamic multi-scale large-kernel attention module (DMLKA), a spectral-spatial attention module (SSAM), and a dynamic upsampling module (DySample). This integrated approach significantly enhances the model's perceptual capability and segmentation accuracy for water body regions.  
The DMLKA module enhances the model's ability to capture multi-scale contextual information by employing large-kernel convolutions and a temperature-regulated dynamic weighting mechanism. Meanwhile, the SSAM incorporates a selective spatial-spectral feature screening mechanism within skip connections, thereby improving the model's detection accuracy for small water bodies and edge regions. Additionally, the DySample module achieves high-precision upsampling by learning sampling offsets, effectively mitigating the boundary artifacts and information loss inherent in conventional methods.  
Experimental results on two public remote sensing datasets (GID and LoveDA) demonstrate that DMLU-Net achieves superior performance compared to existing state-of-the-art CNN- and Transformer-based models, with notable improvements in key metrics, including the IoU (90.46%) and F1-score (94.50%). These results validate the effectiveness and robustness of the proposed method in complex scenarios. Furthermore, ablation studies confirm the critical contribution of each module, substantiating their essential roles in enhancing overall model performance. A model complexity analysis reveals that the model demonstrates a trade-off between computational precision and efficiency when executed on GPUs. However, if this model is to be applied in emergency scenarios or deployed as a simple system, then in-depth research on actual application scenarios is still required.  
Future research will focus on making improvements in four key areas: (1) the model lightweight design, (2) multi-source remote sensing data adaptation, (3) uncertainty modeling, and (4) semi-supervised learning strategies. These improvements aim to enhance the model's computational efficiency, generalization capability, and practical utility, thereby better supporting critical applications, including water resource monitoring, environmental protection, and disaster early warning systems.  
Conceptualization, Z.X. and M.L.; methodology, Z.X. and M.L.; software, Z.X. and M.L.; validation, Z.X.; formal analysis, Z.X.; investigation, Z.X. and M.L.; resources, H.G.; data curation, Z.X. and M.L.; writing--original draft preparation, Z.X. and M.L.; writing--review and editing, Z.X. and H.G.; visualization, Z.X.; supervision, H.G.; project administration, H.G.; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The experiment was conducted based on publicly available datasets. The datasets can be obtained from [[https://x-ytong.github.io/project/GID.html](https://x-ytong.github.io/project/GID.html)]([https://x-ytong.github.io/project/GID.html](https://x-ytong.github.io/project/GID.html)) (accessed on 8 July 2025) and [[https://github.com/Junjue-Wang/LoveDA](https://github.com/Junjue-Wang/LoveDA)]([https://github.com/Junjue-Wang/LoveDA](https://github.com/Junjue-Wang/LoveDA)) (accessed on 8 July 2025).  
The authors thank the anonymous reviewers and the editors for their valuable comments to improve our manuscript.  
The authors declare no conflicts of interest.