## 1 Introduction
[16] introduced strip pooling in DeepLabv3+ to better extract water bodies with a discrete distribution at long distances using different strip kernels. [PERSON] et al. [17] proposed a dual encoder structure to correctly extract small lake water bodies while suppressing noise interference, which can improve target accuracy. [PERSON] et al. [18] designed an adaptive multi-scale feature learning module to replace traditional serial convolution operations, integrating the spectral, texture and semantic features of land cover at different scales so that neural networks can selectively emphasize the features of weak sea land boundaries. [PERSON] et al. [19] designed a residual attention-based feature fusion module, aimed to enhance the recovery of fine-grained river details that may be lost in the initial dense skip connections. This module fortified the integration of detailed river texture features with high-level semantic information, thereby minimizing pixel-level segmentation errors in medium-resolution remote sensing imagery. [PERSON] et al. [20] combined transfer learning and deep learning to expand the network's receptive domain, which can extract more detailed information, improve the coherence of river extraction, and make the extracted river water clearer and more coherent. [PERSON] et al. [21] merged residual blocks into the encoder to alleviate the problem of network degradation. In addition, they utilized dilated convolutions to expand the receptive field and enhance feature extraction capabilities, enabling the entire network to capture more spatial information about lake water bodies. [PERSON] et al. [22] proposed an effective unsupervised deep gradient network to generate higher resolution lake regions from remote sensing images. [PERSON] et al. [23] proposed an improved SegNet for aquaculture sea areas. This model added a pyramid convolution module and a convolution block attention module to capture more global image information, effectively improving feature utilization and enhancing the extraction accuracy of aquaculture sea areas. [PERSON] et al. [24] designed a novel framework for SAR image water extraction that employed a Deep Pyramid Pool module and a Channel Spatial Attention Module to capture multi-scale features, effectively integrating high-level semantics with low-level details for precise detection outcomes. To tackle the edge serration and high data dependency faced by transformer-based models when extracting water bodies from SAR images, [PERSON] et al. [25] employed an innovative approach by replacing the regular patch partition with superpixel segmentation and incorporating a normalized adjacency matrix among superpixels into the Multi-Layer Perceptron model. [PERSON] et al. [26] proposed a feature-fused encoder-decoder network in order to mine and fuse the backscatter features and polarization features of SAR images, thereby achieving a more comprehensive and accurate depiction of water streams. [PERSON] et al. [27] proposed a structure-aware CNN-Transformer network with a U-shaped design and an edge refinement module specifically tailored for enhancing the water segmentation accuracy. Currently, deep learning has achieved significant progress in water body extraction. Researchers have proposed various innovative methods. For instance, techniques such as the introduction of strip pooling, dual encoder structures, adaptive multi-scale feature learning modules, and others have been employed to enhance the model's ability to capture water body features and improve the segmentation accuracy. Despite the improvements in the accuracy and efficiency of water body extraction achieved by the aforementioned methods, significant challenges still remain in enhancing the precision of water body extraction, particularly in addressing multi-scale features, blurred boundaries, and noise interference. For example, while the strip pooling proposed by [PERSON] et al.