# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 1 Introduction
In practical applications, features of different scales are important for segmentation tasks, and models that only extract single-scale features often cannot balance the feature importance of multi-scale water bodies, leading to the missed detection of small water bodies or inaccurate boundaries [24; 25]. Moreover, single-scale models require large model parameters and high computational complexity to maintain prediction accuracy, which easily limits their practicality. To overcome the shortcomings of single-scale models, many studies have attempted to design more complex models to improve the abilities to focus on cross-scale features and detect small water body edges. [PERSON] et al. used the ResNet50 network for feature extraction and added a squeeze-and-excitation module in the residual module to better capture cross-scale pixel feature relationships [26]. [PERSON] et al. developed a novel deep learning network by combining ResNet50 and a multi-scale dense fusion (MDF) module, which enhanced feature representation through a position channel correlation module (DANet), significantly improving the accuracy and noise resistance of water body extraction [27]. The ability of DMAM-UNet to recognize fragmented water body features was enhanced by introducing a dual multiplication attention mechanism module (DMAM) and an atrous spatial pyramid pooling module (ASPP) [28]. These studies have proven the viability of multi-scale feature extraction approaches based on composite models. Meanwhile, the sampling methods (such as bilinear interpolation and transposed convolution) of existing object detection models easily lead to information loss and blurred boundaries when restoring high-resolution feature maps, making it difficult to generate high-quality water body boundaries and resulting in a decrease in water body detection accuracy. Some scholars have considered improving the sampling method to alleviate this problem. An improved lightweight U-Net was proposed by [PERSON] and [PERSON] [29], who significantly reduced the model's parameter count by reducing the number of downsampling layers and improving the bottleneck structure, but the effect of improving model accuracy was not significant, and there were still certain limitations in processing complex boundaries. Based on the consideration of the above problems, in order to simultaneously enhance the overall detection accuracy and the ability to identify small water bodies, a new neural network based on a dynamic multi-kernel large-scale attention mechanism--DMLU-Net-- is proposed. The main contributions of this model are summarized as follows:  
(1) To accurately capture cross-scale remote sensing image features, inspired by the research on multi-core mechanisms [30], a dynamic multi-kernel large-scale attention (DMLKA) module is designed. DMLKA uses multi-receptive field convolution kernels and a dynamic weight aggregation mechanism to enhance the model's ability to capture multi-scale features from remote sensing imagery. This mechanism can effectively improve the model's ability to express complex water body structures while maintaining computational efficiency. (2) To enhance the contextual awareness of information transfer between the encoder and decoder and to better discriminate water bodies from non-aquatic features, a spectral-spatial attention mechanism module (SSAM) is used in the skip connection to refine features. The SSAM can reduce non-water background interference and enhance the recognition fidelity of small water features and boundary segments by fusing feature attention in the channel and spatial domains. (3) To accurately restore the contours of complex water bodies, a DySample module [31] is utilized in the decoding process to restore and enhance the spatial resolution and local details of features.