# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 3 Results
### Comparison Experiments on the WHU Dataset  
We have compared the proposed edge-guided building change detection-based algorithm with other change detection methods on the WHU building change detection dataset.  
Figures 5 and 6 show the qualitative comparison results of the multiple methods in a large building change scenario and a small building change scenario, respectively. In order to better observe the results, different colors are used to show the change detection results.  
Figure 5: Change detection results of different methods for large building change scenarios in the WHU 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.  
Comparison results show that FC-EF and FC-Siam-Conc perform with many misses and misdetections in the change detection maps (as shown in the red and green parts in Figures 5 and 6). The change images generated by FCN-PP based on migration learning are intuitively closer to the manually labeled labels, but there are still some missed detection regions in some scenes. Unet++ MSOF and DSIFN have relatively good detection results in target scenes at various scales, but their edge detection is not clear. BIT-CD has good detection results in most scenes. However, it has a large region of false detection under the interference of similar colored features (as shown in the red part in Figure 5i). In the various scenarios, it can be seen that the change maps predicted by the proposed EGHNet are more complete and with clear details. Especially, our EGHNet still performs well under the interference of similar-colored features. This is due to the fact that the edge-extracted module brings additional information to the feature extraction and reconstruction process, which positively impacts the performance of the network.  
Table 1 presents the quantitative comparison of different methods on the WHU dataset. The results demonstrated that the performance of our proposed EGHNet outperforms the other methods. Specifically, its accuracy (Pre) is 93.08%, recall (Rec) is 89.65%, F1 score is 91.64%, and overall accuracy (OA) is 98.74%. These results clearly show the superiority of EGHNet in processing WHU datasets, especially in terms of accuracy and recall, thus achieving significant performance improvements in several metrics.  
Figure 6: Change detection results of different methods for small building change scenes in the WHU 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.