## 4 Experiment
### Comparing Methods  
To validate the efficacy of the proposed method, both qualitative and quantitative analyses were conducted against contemporary cutting-edge techniques using the Yellow River Basin water bodies dataset. The methodologies for comparison encompass FCN [13], PSPNet [46], PVT (Pyramid Vision Transformer) [47], and TransUnet [48]. The first two comparative methods are based on traditional CNN, and the latter two are based on the Transformer architecture. Specifically, FCN uses an inverse convolutional layer to  
Figure 11: Visual assessment of ablation studies in water bodies extraction: White indicates correct detections (where ground truth is water bodies, and detection successful), red indicates missed detections (where ground truth is water bodies, but detection fails) and blue indicates false detections (where ground truth is not water bodies, but is incorrectly detected as such). From left to right: (**a**) Opt image; (**b**) SAR image; (**c**) Ground truth; (**d**) Swin-UNet-Opt; (**e**) Swin-UNet-Sum; (**f**) SiamSwin-UNet; (**g**) SiamSwin-UNet + DCB-ConvNet; (**h**) SiamSwin-UNet + FPA; and (**i**) SiamSwin-UNet + DCB-ConvNet + FPA. up-sample the feature map to achieve pixel-level classification of the image. PSPNet contains a pyramid pooling module that integrates global contextual information. PVT introduces progressively shrinking pyramids while employing finer-grained image chunks as inputs and always has a global receptive field. TransUnet employs a hybrid encoder of a CNN and a Transformer of hybrid encoders, where Transformer encodes labeled image chunks from Convolutional Neural Network (CNN) feature maps as an input sequence for extracting global context. In this case, TransUnet uses the serial structure of Transformer and CNN in the encoding stage, while our DSST-UNet uses the parallel mode. In addition, SegFormer [49] stands out for its integration of a decoder with a full MLP structure. This design choice allows SegFormer to have a larger and more efficient receptive field compared to regular CNN models, while still maintaining a lightweight architecture. Furthermore, our study incorporates FAN [50], which introduces a novel attentional channel design to the Vision Transformer (ViT) architecture. By dynamically reweighting channel selection based on content, FAN enhances the network's feature extraction capabilities. In our comparison, we utilize SegFormer with FAN Backbone as a comparative method, referred to as SegFormer-FAN. All of the above comparison methods use both optical and SAR images as inputs. Our comparison results are shown in Table 2. From Table 2, we can see that our method has obvious advantages in all indicators and compared with the highest accuracy of 92.64% of mainstream methods, our method can improve the accuracy by about 1%. In the comparative results depicted in Figure 12, the analyses are segregated into four distinct categories. The initial row showcases large lakes with complex perimeters, followed by the second and third rows that illustrate riverine landscapes. The fourth row depicts aquaculture organized in block formations, while the fifth and final section focuses on urban water features. The first row is a frozen lake, and the extraction results of different methods are somewhat misdetected and underdetected. Since FCN is only a simple convolution operation, it has a large number of missed detections on the frozen lake. The Transformer-based network architecture performs better in the face of this problem. Our method can adaptively adjust the receptive field size for local feature enhancement learning, so the extraction results are closer to the labels than these comparison methods. Farmland exists beside the fine rivers in the second row, and the fine tributaries in the large rivers in the third row have complex features. The pooling operation in PSPNet omits the local information of the image, which leads to the breakage of the extraction results of the fine water bodies.