# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 1 Introduction  
Water body extraction from remote sensing imagery is a key technology in water resource management, flood monitoring, environmental assessment, and other fields [1]. With the increase in global climate change and human activities, the distribution of and dynamic changes in water resources have increasingly significant impacts on ecosystems and human society. The accurate and efficient extraction of water body information not only facilitates the rational allocation and utilization of water resources but also provides important support for flood warnings, drought monitoring, and environmental protection [2]. Owing to its extensive coverage and high temporal resolution, remote sensing imagery has become the dominant data source for water body identification. In its early stages, remote sensing-based water body extraction research mainly used spectral attributes and basic threshold segmentation techniques, which emerged in the 1980s [3, 4, 5]. However, these methods, which are simple and easy to use, rely on manually set thresholds and struggle to distinguish complex and variable information categories in high-resolution remote sensing images. They often face many challenges in practicalscenarios [6]. With the development of machine learning techniques, many studies have used machine learning methods instead of spectral index methods for automated water body detection, including logistic regression [7], support vector machines [8], neural networks [6], and clustering [9; 10]. Machine learning methods, by combining spectral, texture, and shape features, have improved the accuracy of water body extraction to a certain extent; among these methods, neural networks show superior performance [6]. However, in tasks involving complex water boundaries and multi-scale water body detection, the perception ability of ordinary neural networks represented by multi-layer perceptrons remains limited [11]. Remote sensing image extraction has found new applications due to the rapid development of deep learning technology in recent years. Convolutional neural networks (CNNs), with their powerful feature extraction capabilities, have achieved remarkable results in remote sensing image classification, object detection, and segmentation tasks [12; 13; 14]. CNNs capture features through multi-layer convolution and assign classification labels to each pixel. On this basis, researchers have proposed various improved CNN models, such as FCN [15], U-Net [16], SegNet [17], DeepLab series [18], and PSPNet [19; 20]. In particular, encoder-decoder-structured CNN models such as U-Net are widely used in water body extraction tasks because they can capture both local and global features [21]. Traditional U-Net employs convolution and pooling to extract image semantic features; however, the limitation of its local receptive field impedes the comprehensive capture of global information, causing inferior performance in processing large-area water bodies. Moreover, although the skip connections of U-Net can transfer encoder features to the decoder, this simple concatenation method cannot effectively fuse and optimize features, easily leading to feature redundancy or loss and affecting recognition accuracy. To capture global sequence semantic features, the attention mechanism widely used in natural language processing (NLP) has been applied to improve water body extraction models. For instance, [PERSON] et al. substituted the U-Net feature extractor with a MixFormer network and integrated an attention mechanism into the skip connection components, leading to a significant enhancement in water body extraction accuracy [22]. [PERSON] et al. optimized the network's learning efficiency while preserving feature map detail information, which was achieved by enhancing the attention mechanism and integrating a spatial pyramid pooling module [23]. However, ordinary attention mechanisms (such as the SE module, CBAM, etc.) mostly focus on a single scale and cannot capture multi-scale feature information simultaneously.