# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 1 Introduction
[15] developed the D-LinkNet model, which integrates dilated convolution modules into the U-Net model to expand the receptive field and enhance the recognition capacity of large-scale objects in the image by scaling and cropping the input image to various dimensions [16]. Nonetheless, the capacity to discern multi-scale objects in the image remains insufficient, potentially leading to the loss of road details, false positives, and false negatives, which negatively impact the road segmentation outcome. To address these constraints, several empirical studies [2; 17; 18; 19; 20; 21] have demonstrated that road extraction algorithms can be improved through the fusion of multi-scale spatial features. For fixed-scale input images, the information acquired by feature extraction operators and classifiers remains constant. Insufficient information can result in improper classification, while excessive information can impede target identification. Consequently, [PERSON] et al. [22] proposed the DeepLab series of networks, utilizing dilated convolutions instead of upsampling and combining dilated convolutions with atrous spatial pyramid pooling modules to augment the receptive field and obtain multi-scale features, thus enhancing the model's multi-scale prediction capabilities. However, the feature extraction method based on dilated convolutions can easily lose information related to small-scale targets, as the convolution kernels solely extract features from restricted regions. [PERSON] et al. [23] introduced the HRNet, which employs a multi-branch structure to simulaneously maintain high- and low-resolution features and repeatedly performs multi-scale fusion to generate rich high-resolution representations. Nevertheless, in occluded images, some resolutions may exhibit information deficits due to partial regions being obscured, ultimately affecting the effectiveness of the resulting feature maps. To effectively address the aforementioned challenges and further enhance road extraction performance, this paper presents a deep learning network model, HRU-Net, specifically tailored for road features in remote sensing imagery, drawing inspiration from U-Net and HRNet. The HRU-Net model inherits and incorporates the encoder-decoder architecture of the U-Net, establishing an information propagation pathway for replicating low-level features to their corresponding high-level representations, thus enriching high-level semantic features with the finer details of low-level features. The model employs a parallel structure within the sub-network to simultaneously preserve high- and low-resolution semantic information. Concurrently, multiple UMR and MPF modules are designed between the encoding and decoding components to optimally exploit multi-scale information. Both modules progressively combine feature maps of different resolutions, integrating the global contextual information of low-resolution feature maps with the robust detail information of high-resolution feature maps to generate high-resolution feature map representations. Furthermore, the different resolution feature maps acquired after fusion by the two modules undergo frequent information exchange via a parallel structure, enhancing the utilization of advantageous information, such as regions and boundaries, and mitigating the impact of extraneous information, such as shadow occlusion. This process yields a high-resolution feature map through consistent information interaction, ensuring the final prediction results closely approximate pixel-level accuracy and achieve a more precise local feature discrimination capacity. The proposed HRU-Net offers the following contributions: the design of UMR and MPF modules within the network. The UMR module is a multi-scale fusion module with upsampling functionality, merging features of varying resolutions post-upsampling to generate larger resolution features for subsequent input into the subnet. In the MPF module, features of identical resolution are combined and decoded synchronously via upsampling.