# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 1 Introduction  
Lakes play an important role in agricultural irrigation and regional ecological environment regulation [1; 2; 3; 4]. With the impact of climate change and human social activities, the reduction of lake area and water body pollution have become urgent problems [5; 6; 7]. Obtaining information about lakes and monitoring changes in lake water bodies are important in preventing and controlling pollution, rationally developing water resources, and building an ecological civilization [8; 9]. The traditional way of extracting water bodies, which relies on manual measurements and water body monitoring stations, is limited by environmental factors and has low timeliness. In recent years, high-resolution satellite images have become more and more popular, providing a reliable data source for the extraction of lake water bodies from remote sensing images. For long distances, complex terrain and inaccessible areas, remote sensing satellites have the great advantages of contactless measurement, short imaging cycles, and high work efficiency. Therefore, the rapid and accurate extraction of water body information from remote sensing satellite images has great application value. The key of water body extraction is to enhance the difference between the water body and the background part,to find the boundary between the water body and the background, and to achieve the purpose of separating the water body and the background. The band threshold method is the water body extraction method for early remote sensing images, but the accuracy and stability of this method may be affected in the case of complex backgrounds, illumination changes and poor data quality [10; 11; 12]. In practical applications, the classifier method is commonly used in order to improve the accuracy and reliability of water body extraction [13; 14; 15]. Although the classifier method integrates remote sensing image information such as spectra, shape and feature texture, the influence of sample selection, segmentation threshold and classification criterion leads to insufficient model generalization ability, which cannot be adapted to the water body extraction project with large data volume. The full convolutional neural network model based on deep learning realizes the whole process of lake water body extraction from remote sensing images without manually set parameters, which can automatically and deeply segment the image based on each pixel point and the regional characteristics of the pixel, and it is excellent in both extraction speed and accuracy. [PERSON] et al. proposed fully convolutional networks (FCN), which are capable of accomplishing end-to-end semantic segmentation and significantly improving target segmentation accuracy [16]. [PERSON] et al. constructed U-Net on top of FCN to extract features by decreasing the image height and width while increasing the image dimensions through an encoder and restoring the image through a decoder [17]. [PERSON] et al. introduced residual learning into various algorithms of deep learning, which substantially improved the extraction accuracy of semantic segmentation [18]. [PERSON] et al. used the model of mask region with convolutional neural networks (R-CNN) based on ResNet50 and ResNet101 to automatically detect and extract water bodies in remote sensing images with good accuracy [19]. [PERSON] et al. established a multi-scale water body extraction CNN to automatically extract water bodies in Gaofen-1 remote sensing images [20]. [PERSON] et al. proposed to use the spatial pyramid pooling module to combine feature maps at different scales in DeepLab V3+ [21]. [PERSON] et al. improved on the U-Net model of the encoding-decoding architecture and proposed a SER34 AUNet model, which finally achieved good results in the extraction of water body experiments [22]. [PERSON] et al. made MU-Net a new method for automatic extraction of water bodies, which is based on U-Net for modeling the local spatial detail information and global contextual information of images [23].