# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 3 Experiments and Results
### Ablation Experiment  
#### 3.4.1 Exploring the Impact of Modules on the Network  
To evaluate the roles of the UMR and MRF modules in the HRU-Net model, a series of experiments was conducted, and a detailed analysis was carried out on their performance in the task of road extraction.  
Initially, both the experimental and control groups were designed for comparative assessment. The control group consisted of the full HRU-Net model, incorporating both the UMR and MRF modules, while the experimental group was modified by excluding either the UMR or MRF module. All other experimental parameters and datasets were held constant to ensure a fair comparison.  
Throughout the experiment, a diverse set of high-resolution remote sensing images was used, and identical training and validation procedures were applied to train and evaluate the various models. Evaluation metrics, including precision (P), recall (R), and intersection over union (IoU), were employed to gauge the performance of road extraction, as detailed in the Table 4.  
In evaluating the UMR module, performance differences between the experimental group (excluding the UMR module) and the control group (HRU-Net) were compared. A similar comparison was carried out for the MRF module. The training was conducted  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Precision (\%)** & **Recall (\%)** & **IoU (\%)** \\ \hline Remove UMR & 78.92 & 83.14 & 77.91 \\ Remove MRF & 79.19 & 83.47 & 78.06 \\ HRU-Net (ours) & 80.09 & 84.85 & 78.62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: UMR and MRF module performance accuracy comparison. The closer the index value is to 1, the better the effect. IoU: Intersection over union.  
Figure 5: Comparison of road extraction effects of 3 schemes in 4 different test areas in DeepGlobe Road Dataset.  
using the same dataset and experimental parameters, with the performance results on the Massachusetts Road Dataset shown in Figure 6.  
Following the exclusion of the MRF module, the model's road edge extraction in some complex scenes was observed to be less precise, with blurred edges, breaks, or omissions. The MRF module played a key role in the HRU-Net model, effectively integrating multi-feature fusion and information transmission. By introducing the MRF module at the decoding stage, the model was better able to capture local road details and contextual relationships, thus enhancing its road extraction performance. Simultaneously, road discontinuities were observed when the UMR module was removed.  
More specifically, in some complex scenes, the road continuity was disrupted, leading to interruptions or incomplete road segmentation. This might be due to a weakened ability.  
In conclusion, the inclusion of the UMR and MRF modules in the HRU-Net model played a pivotal role in enhancing the performance of the road extraction task. Their design philosophies and functions were complementary, synergistically boosting the model's perception capabilities, feature expression ability, and accuracy. Consequently, they provided an effective solution for road extraction in high-resolution remote sensing images.  
Figure 6: Performance experiment comparison of UMR and MRF modules.