# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 1 Introduction
Although these above methods have greatly improved the accuracy and efficiency of water body extraction, the improvement of water body extraction accuracy still faces major challenges such as multi-scale features, noise interference and boundary blurring. The classical FCN encoder has fewer layers, cannot fully extract features, and loses some detail information in the process of up-sampling to recover the resolution, which leads to a lack of clarity in the boundary of the segmentation results, especially for the recognition of small targets or fine structures. U-Net may have fuzzy boundaries for the segmented objects, especially at the target edge position, and the segmentation results are not fine enough. In addition, U-Net is sensitive to the scale of the input image, which may lead to degradation of the segmentation effect when dealing with images with mismatched scales. Mask R-CNN contains multiple components, and this complexity leads to higher computational cost and training time, and the segmentation results may have blurred boundaries in the edge region of the target object. If the noise introduces inconsistent textures or features, it may cause the DeepLab V3+ model to produce unstable predictions in noisy regions. To address these limitations, we propose a novel lake water body extraction model, R50A3-LWBENet, which uses a more complex ResNet50 network structure and introduces methods such as shortcut connection, attention mechanism, and up-sampling to solve the problems of loss of fine information, insufficient semantic information, information transfer limitations, and boundary blurring. The combined application of these methods helps to improve the performance of the model in dealing with multi-scale features, noise interference, and boundary blurring. The study includes (1) Encoding, with ResNet50 as the backbone network capturing more abstract and rich feature representations to accurately recognize and segment different classes of objects and regions. The structure of the residual block itself in ResNet allows the network to reuse features at different layers, thus providing more multi-scale information in the decoder part, which helps to solve the multi-scale feature fusion problem in semantic segmentation. (2) Incorporating squeeze and excitation (SE) block into the residual module of ResNet50, which enables the SE-ResNet network to take into account the importance of each feature map during the feature extraction process, to strengthen the useful information and suppress the useless information. (3) After the feature extraction is completed, the convolutional block attention module (CBAM) is applied to pay attention to the channel and spatial features of all the feature maps at the same time to remove the noise interference and improve the accuracy of the edges of the lake water body. The introduction of the attention mechanism helps the network to pay more attention to important feature information, and the network can dynamically adjust the weights of the feature map to enhance the features that help segmentation according to the importance of the features, and reduce the noise and unnecessary information, which helps to improve the model's attention to the key features, and at the same time inhibit the over-reliance on noisy and other interfering information so as to improve the accuracy and robustness of semantic segmentation. (4) Decoding, the feature maps are up-sampled using bilinear interpolation to bring the low resolution feature maps to their original resolution. The up-sampled feature maps are fused with the high-resolution feature maps obtained by shortcut connection, a mechanism that enables the decoder to utilize both shallow and deep features in the encoder. Shallow features typically contain more local details, while deep features carry more advanced semantic information.