## 1 Introduction  
Lakes are one of the carriers to measure the ecological environment of a region. The change in lake area can reflect the regional hydrology and climate changes [1]. It has long been an important demand for ecological and environmental protection workers to obtain satellite images of the lake research area through remote sensing methods and accurately segment the water body region [2]. In recent years, the extraction methods of water body in remote sensing images include band combination [1], computer vision method, threshold method, and band index method [3]. Such methods use the difference of reflected bands of water body in remote sensing images as the standard for segmentation. For example, the normalized difference water index (NDWI) is inspired by the sensitivity of plants to red bands and infrared bands,the difference of green bands and near infrared bands are used to normalize the extraction index of water body [4]. Later, in order to adapt to the task of water body segmentation in cities, [PERSON] [5] proposed the modified normalized difference water index (MNDWI) for investigating water extraction from remote sensing images. However, using the difference of object reflected bands as the basis for segmentation will cause the situation of \"different objects with the same spectrum\" and \"different spectrum of the same object\". Therefore, designing a more accurate method for lake extraction is still an urgent task to be solved [6]. With the development of the neural network, semantic segmentation algorithm has become a hot research topic in the task of remote sensing image water extraction [7]. In the development of semantic segmentation algorithm, fully convolutional network (FCN) is a milestone research [8]. The proposed fully connected structure makes convolutional neural network (CNN) better applied to feature extraction of images, and classifies semantic segmentation tasks accurately to the pixel level. In 2015, U-Net algorithm was designed to achieve the semantic segmentation tasks of medical images. The pioneering idea of U-Net model is the designed encoder and decoder structure. However, the limitation of the U-Net model is that the background information and target information are transmitted at the same time after feature extraction, which hampers the model's learning of image features [9]. In the same year, in order to solve the complexity caused by the model's continuous stacking of convolution features, [PERSON]'s team [10] proposed the residual network framework, which uses the idea of a residual function to change the depth of simple convolution replacement, greatly reducing the calculation of the model and becoming an efficient network model. In 2016, [PERSON] developed pyramid scene parsing network (PSP-Net) for semantic segmentation tasks across diverse scenarios and introduced the pyramid pooling module [11] to enhance feature information from various extraction layers while expanding the network's receptive field. In 2017, [PERSON] introduced the transformer network architecture for natural language processing (NLP) field. The concept of attention emerged as a pivotal component in the development of transformer [12], and subsequently became a significant model for computer vision tasks. In 2018, [PERSON]'s [13] team extended the DeepLabV3 model by incorporating deep separable convolution into a pyramid pooling operation. They further devised the DeepLabV3+ model to refine the boundaries of target objects in semantic segmentation, which has gained recognition as an established semantic segmentation approach. [PERSON] et al. [14] integrated attention-gates into the U-Net model, marking its pioneering utilization of attention mechanism to enhance learning capability. In the field of water extraction, numerous scholars have optimized and enhanced existing models to improve the accuracy of segmentation for water extraction. For instance, [PERSON] et al.