## 1 Introduction
The emergence of a Transformer model, a sequence-to-sequence architecture based on the attention mechanism, heralds new prospects. Unlike traditional CNNs, a Transformer leverages multi-head attention to establish remote dependencies, thereby facilitating a more holistic feature extraction from images [20]. This technique is gaining traction in water bodies extraction [11; 21]. [PERSON] et al. [11] introduced Convolutional Block Attention Module (CBAM) and substituted the conventional convolutional layers in the original U-Net model encoder with Vision Transformer (Vi-T), enhancing the continuous spatial relationship interpretation, and thereby improving the segmentation of lake water bodies on the Qinghai-Tibet Plateau. However, Transformer architecture, which compresses the image into one-dimensional tokens, interpreted as sequences, has its limitations. Specifically, this approach can result in the loss of internal and local image information, potentially impeding the recovery of fine-grained details during the decoding stage [21]. The fusion of optical and microwave images emerges as a robust solution, providing complementary visuals and fortifying the database requisite for feature identification [22; 23; 24]. Initial research efforts primarily focused on the amalgamation of multispectral and radar data through shallow feature fusion, as demonstrated in studies by [PERSON] [25]. These classical classifiers rely on feature engineering and exhibited limited generalization proficiencies. Inspired by deep learning in Computer Vision (CV), the powerful feature extraction capability of ConvolutionalNeural Networks (CNNs) has been successfully applied in feature classification, with contemporary research gravitating towards multimodal learning. In general, we note that there are still some problems when conducting fine extraction and dynamic monitoring of large-scale water bodies: (1) For water bodies with variable shapes and complex scenes, existing deep learning studies use convolution kernels to encode key features, which lack global information between pixels, resulting in inconsistent water extraction results and inaccurate lake extraction edges. (2) Continuous down-sampling of convolution operations will lead to information loss, and the extraction results of small water bodies will be off-flow. In order to solve the above problems, the contributions of this paper are as follows:  
1. Using the parallel coding structure of Swin-T and CNN, while adaptively enhancing the local features of the image, the global association between pixels is strengthened, improving the accuracy of water boundary extraction and the consistency of the results. 2. An attention mechanism module is proposed, which automatically highlight the important channels of the feature map according to the characteristics of the image feature map and avoids the problem of excessive attention of convolution on irrelevant local features. 3. In the advanced feature coding stage, the FPA module is used to construct the feature pyramid to obtain the advanced semantic features of different scales of the image and improve the segmentation ability of multi-scale water bodies. We construct a water extraction network based on multi-source data, and use the network to extract water bodies, achieving high segmentation accuracy. In addition, we also mapped the seasonal water body and permanent water body of the Yellow River basin in 2021 and compared them with existing water body products.