# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Discussion
#### 3.4.4 Model Limitations and Future Work  
Although the proposed DMLU-Net achieved state-of-the-art performance in water body extraction from remote sensing images and demonstrated strong generalization abilities across multiple datasets, several aspects merit further investigation and optimization:  
(1) Model compression potential: While DMLU-Net improves the extraction accuracy through its DMLKA module, SSAM, and DySample module, these enhancements come at the cost of increased parameters and computational overhead, particularly limiting its efficiency in processing high-resolution imagery. This limitation may make it difficult for the model to be applied to the rapid survey of large-scale areas in emergency scenarios. Future work could explore structural pruning, lightweight attention alternatives, or knowledge distillation to substantially reduce inference costs while preserving accuracy, thereby enhancing the feasibility of deployment on resource-constrained platforms (e.g., satellite terminals or mobile devices).  
(2) The model's adaptability to multi-source heterogeneous data remains to be systematically evaluated. While current experiments focus on single-sensor optical imagery, practical remote sensing applications typically involve multi-source (e.g., SAR, Landsat, and Sentinel) and multi-modal (e.g., hyperspectral and multi-temporal) data. DMLU-Net's performance in cross-sensor, cross-resolution, and cross-modal scenarios requires further validation. For some common problems in optical images (such as SAR speckle noise and hyperspectral dimension redundancy), subsequent measures will be taken based on this study to alleviate them through inter-modal pre-training (initializing part of the network using public non-optical datasets) and targeted data augmentation (such as SAR speckle suppression and hyperspectral dimension reduction/clustering). In addition, another possible solution is to build an ensemble learner by using a model for optical images and the model proposed in this study. Future research will focus on enhancing the model's generalization ability in the complex data environment of the real world.  
(3) This study lacks a systematic exploration of uncertainty modeling and interpretability. Although DMLU-Net performs well in numerical evaluation, the model's predictive capability for anomalous areas, fuzzy boundaries, or low-contrast water bodies is not fully demonstrated. Future research can introduce uncertainty quantification mechanisms (such as Monte Carlo dropout and Bayesian deep learning) to evaluate the reliability of model predictions, and it can combine interpretability methods (such as CAM/Grad CAM) to enhance the controllability and trustworthiness of the model in practical scenarios.  
(4) Strong data dependence and high annotation costs. Current model training relies on large-scale, high-quality pixel-level annotated data, but the manual annotation of remote sensing images is costly and time-consuming. Future research could explore semi-supervised learning, pseudo-label generation, self-supervised contrastive learning, and similar approaches to reduce the reliance on manual annotations and enhance model adaptability in data-scar scenarios.