## 1 Introduction  
The utilization of synthetic aperture radar (SAR) images, characterized by their all-weather and cloud-penetrating capabilities, holds significant application value for surface water detection. By leveraging the obtained water detection results, timely insights into changes in flood conditions, water environments, coastlines, and environmental protection can be gained. Consequently, this provides vital information and guidance for national disaster prevention, mitigation, rescue efforts, and comprehensive development planning [1, 2, 3, 4]. Remote sensing image-based techniques for water body extraction can be broadly categorized into two primary groups: classification rule-based and deep-learning-based. The method based on classification rules relies on the unique spectral reflectance characteristics of water bodies in optical images. Water body recognition and extraction can be achieved through manually set classification criteria, such as the utilization of water body indices [5], the construction of decision tree models [6], the application of support vector machine methods [7], the introduction of physical approaches [8; 9], and the adoption of object-oriented analysis strategies [10]. However, such methods are limited by subjective judgment, which not only increases the workload, but also may affect the extraction accuracy due to subjective bias. Especially in the face of the diversity of different sensor data and the complexity of surface features, their accuracy and detail retention ability may be challenged. Deep learning-based methods have demonstrated powerful automatic feature learning and recognition capabilities. They do not need to pre-set complex classification rules but can autonomously discover the deep-level features of water bodies from massive data and continuously optimize the representation of these features through model training, thus demonstrating significant advantages in extraction accuracy and generalization. Deep learning utilizes convolutional neural networks to learn high-dimensional and multi-scale information such as the color, texture, size, and position of target objects, forming extraction models for different objects. Since [PERSON] proposed deep learning [11] in 2006, an increasing number of water detection methods based on deep learning have emerged and become mainstream, achieving automation in water detection. [PERSON] et al. [12] proposed fully convolutional networks (FCNs), which can achieve end-to-end semantic segmentation and significantly improve the accuracy of target segmentation. [PERSON] et al. [13] constructed U-Net for the semantic segmentation of medical images. It employs a symmetrical encoder-decoder architecture, which preserves the spatial information of the images. By utilizing skip connections, low-level features are transferred to the decoder, thereby enhancing the detail of the segmentation. To address the issue of vanishing or exploding gradients encountered during the training of deep neural networks, [PERSON] et al. [14] designed the Residual Neural Network (ResNet). ResNet enables information to propagate more effectively within the network, thereby overcoming the challenges faced by traditional deep neural networks during training. This advancement has propelled further developments in deep learning technology. [PERSON] et al. [15] incorporated depthwise separable convolutions into atrous spatial pyramid pooling (ASPP) operations. They designed the DeepLabV3+ model to refine the boundaries of target objects in semantic segmentation. This effectively addressed issues such as the resolution loss, inadequate feature extraction, and limited generalization ability faced by traditional convolutional neural networks when tackling semantic segmentation tasks. DeepLabV3+ has been widely recognized as a mature approach to semantic segmentation. In the realm of water body extraction, numerous researchers have refined and enhanced existing models with the aim of increasing the accuracy of water body extraction. For instance, [PERSON] et al.