# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 3 Results
### Comparison Experiments on the LEVIR-CD Dataset  
In the comparison experiments of the LEVIR-CD dataset, two building change scenarios in the LEVIR-CD dataset were selected for analysis, namely: large irregular building change and small densely distributed building change scenarios. Figures 7 and 8 show the qualitative comparison results under the above two change scenarios in the LEVIR-CD dataset, respectively. For better observation, different colors are used to show the change detection results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **PRE (\%)** & **REC (\%)** & **F1 (\%)** & **OA (\%)** \\ \hline FC-EF\({}_{18}\) & 74.56 & 73.71 & 73.14 & 96.29 \\ FC-Siam-Conc\({}_{18}\) & 83.96 & 79.32 & 80.52 & 97.52 \\ FCN-PP\({}_{19}\) & **92.00** & 83.02 & 86.54 & 98.37 \\ Unet++MSOF\({}_{19}\) & 89.18 & 87.82 & 87.88 & 98.39 \\ DSIFN\({}_{20}\) & 72.62 & 88.00 & 79.21 & 97.83 \\ BIT-CD\({}_{22}\) & 88.50 & **90.24** & **89.35** & 96.99 \\ ChangeFormer\({}_{23}\) & 89.69 & 86.43 & 88.03 & **98.95** \\ HSAA-CD\({}_{24}\) & 85.55 & 83.54 & 84.56 & 98.39 \\ EGHNet & **93.08** & **89.65** & **91.14** & **98.74** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison results of quantitative metrics of different methods on the WHU dataset. The best two Results are in bold and underlined. Figure 7: Change Detection Results of Different Methods for Large Irregular Building Change Scenarios in LEVIR-CD Dataset. (**a**) Prechange image; (**b**) post-change image; (**c**) label; (**d**–**j**) are the detection results of FC-EF [31], FC-Siam-Conc [31], FCN-PP [33], UNet++MSOF [20], DSIFN [34], BIT-CD [32], and the proposed EGHNet, respectively. In the color classification, the true positive is white, the true negative is black, the false positive is red, and the false negative is green. From Figures 7 and 8, it can be seen that among the experimental comparison methods, FC-EF has a weaker detection capability, with better detection results only for large buildings, and cannot detect the target well within various sparse buildings and dense building clusters. The other methods also suffer from different degrees of misdetection, among which FC-Siam-Conc and Unet-MSOF have more misdetections. In contrast, FCN-PP and DSFN misdetections are improved, but there is still a part of missed detection. Our proposed EGHNet method is able to accurately detect the changed region and capture the edge of ground objects in various scenarios, which is very close to the manually labeled labels from the visual point of view. Table 2 demonstrates the performance of different methods on the LEVIR-CD dataset. The proposed EGHNet performs well in several aspects, where F1 and OA perform optimally among all the methods with 89.76% and 99.02%, respectively; PRE and REC perform sub-optimally with 91.56%, 88.23%, and 89.24%, respectively. This result proves the effectiveness of the proposed algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **PRE (\%)** & **REC (\%)** & **F1 (\%)** & **OA (\%)** \\ \hline FC-EF\({}_{18}\) & 78.73 & 79.78 & 78.72 & 98.27 \\ FC-Siam-Conc\({}_{18}\) & 85.39 & 85.93 & 85.44 & 98.84 \\ FCN-PP\({}_{19}\) & **91.58** & 84.17 & 87.54 & **99.01** \\ Unet++MSOF\({}_{19}\) & 88.52 & 85.29 & 86.69 & 98.94 \\ DSFN\({}_{20}\) & 85.61 & 86.63 & 85.48 & 98.97 \\ BIT-CD\({}_{22}\) & 89.24 & **89.37** & **89.31** & 98.92 \\ ChangeFormer\({}_{23}\) & 89.39 & 87.73 & 88.56 & 98.81 \\ HSAA-CD\({}_{24}\) & 89.14 & **88.83** & 89.02 & 98.83 \\ EGHNet & **91.56** & 88.23 & **89.76** & **99.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative results of quantitative metrics of different methods on the LEVIR-CD dataset. The best two results are in bold and underlined. Figure 8: Change Detection Results of Different Methods for Small Densely Distributed Building Change Scenarios in LEVIR-CD Dataset.