## 4 Experiment
### Ablation Study  
Ablation study dissects the performance contributions of various configurations within the Swin-UNet architecture as follows:  
1 Swin-Unet + Optical: This configuration employs U-shaped architecture of the Swin-UNet network, designated as \"Swin-UNet-Opt\". It exclusively utilizes optical image patches as input. 2 Swin-Unet + Pixel-level fusion: Architecturally akin to \"Swin-UNet-Opt\", this variation, termed \"Swin-UNet-Sum,\" also relies on U-shaped Swin-UNet framework. However, it diverges by inputting patches derived from a pixel-wise fusion of optical and SAR imagery. 3 SiamSwin-UNet + feature-level fusion: SiamSwin-UNet model is an evolution of Swin-UNet structure, characterized by a dual-branch design. These branches operate independently, maintaining distinct weights and input samples. The first branch processes optical image patches, while the second handles SAR image patches. To validate the individual contributions of each component within DSST-UNet framework, this study conducted systematic ablation tests using the Yellow River Basin dataset. We established Method 1 as the Baseline, utilizing only optical input. Method 2 extends this by incorporating pixel-level fusion of multisource data, while Method 3 evolves the structure to a dual-branch SiamSwin-UNet, employing feature-level fusion. Subsequent iterations, labeled Methods 4 through 6, sequentially integrate DCB-ConvNet and FPA modules, either individually or combined, into the SiamSwin-UNet structure, thereby allowing an assessment of each configuration's impact on performance enhancement. The ablation study results, detailed in Table 1, demonstrate that feature-level fusion of multi-source data outperforms pixel-level fusion in water bodies extraction, with the latter surpassing the use of solely optical data. Accordingly, feature-level fusion is adopted as the  
Figure 10: Sample repository mapping: (a) Lakes; (b) Rivers; (c) Urban water bodies; (d) Aquaculture. Presented in descending order. standard for subsequent analysis. Employing just a simple feature-level fusion in SiamSwin-UNet yielded an F1 score of 91.62, indicating potential underutilization of features. The introduction of a DCB-ConvNet branch, which incorporates deformable convolutions in CNN operations, addressed this by enhancing local detail processing, resulting in a 2% F1 score increase to 93.58. Incorporation of a FPA module further improved the F1 score to 92.78, a 1% rise, and augmented Recall by 1%, diminishing detection omissions. This enhancement stems from FPA's ability to guide the network's focus towards salient high-level features, thereby mitigating detail loss. The concurrent application of both the DCB-ConvNet branch and the FPA module significantly bolstered extraction efficacy. The DCB-ConvNet branch fortified local feature learning, the deformable convolution diversified the adaptability to various water body types, and the FPA module minimized detail neglect. Collectively, this approach harmonized accuracy and Recall metrics for water bodies extraction, culminating in a 2% F1 score leap to 93.7. In the illustrative outcomes depicted in Figure 11, the ablation findings are segmented into four categories. The initial two rows depict lakes with complex perimeters, followed by rivers, geometrically distinct aquaculture formations, and finally, urban water bodies. The first row contains both expansive and diminutive lakes characterized by their intricate borders. These water bodies, set against a relatively non-complex topographical backdrop, are distinguished by their pronounced boundaries, geometric regularity, and contrast relative to adjacent areas, making them perceptibly distinct in color and texture. This result suggests that Transformer's self-attention mechanism is proficient in capturing the image's global context, thereby effectively segregating lakes from other elements. Nonetheless, when dealing with smaller lakes exhibiting complex contours, SiamSwin-UNet framework tends to generate numerous incorrect identifications.