# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 3 Experiments and Results
### Results and Analysis
#### 3.3.1 Test on Massachusetts Road Dataset  
The performance indicators for the six methodologies are presented in Table 2. From the performance comparison table, it is evident that the relatively simple U-Net and HRNet structures exhibited slightly inferior feature extraction performance compared to the other more complex models in the table. The unique residual structure of the ResNet and its deep layer hierarchy provided better semantic expression capabilities. The ResUnet, which combined the residual network and U-Net, offered better network depth than U-Net and superior high-to-low level information transmission ability compared to ResNet, demonstrating enhanced performance in the average accuracy, recall, and average intersection over union metrics. The HRU-Net structure proposed in this paper outperformed all other models in every indicator. To more intuitively compare the road extraction effects of various network models, this paper selected five 512 \(\times\) 512 images from the test data for display and evaluation. To emphasize the road details, we set the roads in the predicted images to be displayed in red. Figure 4 displays the road extraction results from different networks. From top to bottom, it presents the original remote sensing image, the original ground truth label, and the predicted results of U-net (Scheme 1), ResNet (Scheme 2), DeepLabV3 (Scheme 3), ResUnet \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{**Network**} & **Precision** & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**IoU (\%)**} \\  & & **(\%)** & & \\ \hline One & U-Net & 78.82 & 83.76 & 77.67 \\ Two & ResNet & 78.93 & 83.25 & 77.90 \\ Three & Deeplabv3 & 79.56 & 83.90 & 75.45 \\ Four & ResUnet & 79.29 & 83.85 & 77.97 \\ Five & HRNet & 77.96 & 83.90 & 77.54 \\ \hline Six & HRU-Net (ours) & 80.09 & 84.85 & 78.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation of six methods conducted on the Massachusetts Dataset. The closer the index value is to 1, the better the effect. IoU: Intersection over union. (Scheme 4), HRNet (Scheme 5), and HRU-Net (Scheme 6). The road extraction analysis primarily consisted of the main road extraction analysis and the detail extraction analysis of small roads. Shadow occlusion and spectral confusion caused by surrounding materials similar to roads often impacted road extraction completeness. In Scheme 1, numerous instances of inadequate edge detail extraction and disconnection occurred during road extraction. Schemes 2-4 employed the residual network structure and attention mechanism to concentrate on more road detail information, which improved the disconnection situation in road extraction, but some roads remained unextracted. Scheme 5 utilized a multi-scale parallel network structure to address incomplete extraction and enhance road connectivity. Scheme 6, proposed in this paper, ensured the completeness of road extraction and resolved the disconnection caused by shadow occlusion in positions 3 and 5, guaranteeing the completeness of the extracted road at the intersection and significantly improving road extraction efficiency and accuracy. To more clearly analyze the effects, we annotated 11 representative road areas in the images for analysis. Areas 1, 3, 7, and 8 were heavily affected by shadow occlusion; Areas 2, 4, and 5 had significant spectral confusion issues; and Areas 6, 9, 10, and 11 were difficult to extract due to the complexity of the road regions. In Scheme 1, the extraction of the roads at Areas 2, 4, 7, 9, 10, and 11 was hindered by the influence of other objects  
Figure 4: Comparison of road extraction effects of 6 schemes in 5 different test areas in Massachusetts Road Dataset. in the surrounding area with similar materials to the roads. Schemes 2-4 incorporated more road details, extracting roads to varying degrees at Areas 2, 4, 7, and 10. Schemes 5 and 6 frequently exchanged information among different scales, allowing them to fully extract the roads at Area 11.