## 1 Introduction
[15] incorporated depthwise separable convolutions in the encoder to enhance the capacity for extracting global information from remote sensing images when identifying lake water features. [PERSON] et al. [16] devised a dense network for precise water extraction and integrated a bidirectional channel attention mechanism to mitigate noise interference during lake analysis. [PERSON] [17] proposed a multi-scale fusion attention network multi-scale successive attention fusion network to solve the problem of poor accuracy in narrow boundary in water extraction, and realized attention operations on low-level features and high-level features, respectively. [PERSON] [18] improved the DeepLabV3+ model for small water extraction, and applied the idea of generative adversarial network (GAN) to generate an adversarial network to synthesize the original remote sensing image with false color to improve the features of small water bodies. [PERSON] et al. [19] designed the network to improve the accuracy by linking the contextual information of water bodies. [PERSON] et al. [20] improved the SegNet network with the idea of the residual network in the process of lake water extraction, and realized identity mapping to alleviate the training degradation problem in stacked networks. Additionally, [PERSON] et al. [21] reconstructed the DeepLabV3+ network model and optimized it using a fully connected conditional random field (CRF), resulting in a significant improvement in the accuracy of water body segmentation. [PERSON] et al. [22] proposed a lightweight network for extracting water bodies, which not only enhanced edge segmentation accuracy but also reduced the parameters of the extraction network. In order to enhance the detection of water bodies, [PERSON] et al. [23] proposed a multi-scale model to water body extraction for integrating features across different scales. [PERSON] et al. [24] employed a multi-scale dense connection module in the design of the multi-scale lake water extraction network for water extraction, effectively suppressing image noise and achieving the precise extraction of small water bodies. In recent years, the emergence of vision transformer [25] has proven to be successfully applied in various fields of computer vision tasks. In the noise-canceling transformer network (NT-Net) designed by [PERSON] [26], multi-level transformer modules were introduced into the NT-Net network to effectively capture global information related to water features and enhance its ability to distinguish lake boundaries. Furthermore, [PERSON] et al. [27] proposed a U-Net network that integrates transformer modules, utilizing CONV+Mixformer Block for feature extraction and incorporating an attention module for optimal water segmentation performance on the GID datasets. [PERSON] et al. [28] combined the swin-transformer algorithm for lake extraction on the Qinghai-Tibet Plateau lake datasets, replaced the convolution structure of the U-Net encoder with vision transformer (VIT) and added channel-wise and spatial-wise attention module (CBAM) attention mechanism in the decoder. The improved network achieved the best performance compared with other algorithms in the plateau lake extraction task. However, there are certain limitations associated with the aforementioned methods. On the one hand, effectively utilizing attention mechanisms to enhance the model performance poses a significant challenge. Some researchers have improved model performance by directly employing existing attention mechanisms [29; 30; 31]. While the attention mechanism is widely adopted to enhance network segmentation accuracy, its effectiveness is influenced by the feature learning capability of the attention module. Therefore, the feature learning of the attention module is optimized in this study, and the global efficient attention module (GEA) is employed to enhance the extraction of water features.