# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 2 Methods
### HRU-Net  
#### 2.3.1 Multi-Feature Module Construction  
In delivering greater road details, high-resolution remote sensing images also expose distinctions among roads at varying levels, such as road materials, grades, and surrounding environments. To further augment the network's capacity for detail expression and feature extraction, we initially contemplated utilizing a \"U\"-shaped network structure in constructing our method. This structure performs four downsampling operations in the encoder, broadening the receptive field range. It enables features to propagate between low-level and high-level features more straightforwardly, fosters backpropagation in network model training, and supplements additional detail information to high-level semantic features. Nevertheless, while expanding the receptive field through downsampling, some road detail information is lost, proving challenging to recover or even vanishing during forward propagation in the network. Consequently, we designed the UMR and MRF modules to concurrently consider multiple levels of semantic features, capture multiscale information, and contemplate local road detail information. 1. UMR Module  
In this study, as the extraction level deepened, the semantic features obtained gradually deepened and became more abstract. We designed four multi-feature fusion modules (UMRs) in the encoding phase, which fused feature maps from different levels of the network into a high-resolution feature map through upsampling techniques. This design helps to capture and utilize abstract features at each level, thereby enhancing the model's feature expression capabilities. As shown in Figure 1, the UMR module first received feature maps from the previous and the same layer of the network as inputs. Subsequently, we performed bilinear interpolation on the small-sized feature map from the next layer of the network, aligning its size with the other inputs. Afterward, we performed a Concat operation on all adjusted feature maps to merge and further extract features. During the forward propagation process of the model, we used parallel downsampling strategies to process the high-resolution feature map fused after four UMR modules, thereby reducing the size of the output feature map. At the same time, we used the output of this module as the input for path 1 of the next UMR module in order to repeat the Concat connection and downsampling operations, gradually enhancing the resolution of the feature map. 1. MRF Module  
In the decoder part of our study, we set up four multi-resolution feature (MRF) modules to cascade feature maps at different levels for feature extraction. As depicted in Figure 2, the MRF module receives the output feature map of the upsampling multi-feature fusion (UMR) module at the same level in the encoder stage as the input through path 1 and receives the feature map upsampled in the decoder stage through path 2. Initially, two feature maps of the same size were input for a Concat operation, and feature extraction was performed through a combination of two \(3\times 3\) convolutions and ReLU activation functions. Subsequently, the output of this module was connected with the output of the UMR module at the next level and used as the input for the next MRF module for concatenation and feature extraction operations. In this process, we chose the Concat operation over summation because concatenation can retain the original information of the feature maps at each level, avoiding mutual interference of features. Our choice of the combination of \(3\times 3\) convolution and the ReLU activation function aimed to achieve a good feature extraction effect while keeping the number of parameters under control. This design allowed the decoder to effectively utilize multi-scale feature maps generated by the encoder, and the MRF modules iteratively fused and upsampled the feature maps. By concatenating the feature maps at different levels, the network can better capture both high-level semantic information and low-level detail information.