## 4 Experimental Results and Analysis
### Performance Evaluation on the KOMPSAT-3 Dataset
The proposed method consistently generates a good change map for the reversed input order, as shown in Figure 17. The ATTUNet with JF is significantly degenerated for the reverse order, producing many more false positives. Almost all building regions are detected as changes. In addition, Modified-UNet++ yields a very inaccurate change map. Figure 13: Changed detection maps of the proposed and conventional algorithms for “Test area 1” for the reverse input order: (**a**) Image P; (**b**) Image C; (**c**) Ground truth; (**d**) DI + UNet; (**e**) DI + ATTUNet; (**f**) JF + UNet; (**g**) JF + ATTUNet; (**h**) Modified-UNet++; (**i**) Proposed bilateral dissimilarity U-Net. Figure 14: Changed detection maps of the proposed and conventional algorithms for “Test area 2”: (**a**) Image P; (**b**) Image C; (**c**) Ground truth; (**d**) DI + UNet; (**e**) DI + ATTUNet; (**f**) JF + UNet; (**g**) JF + ATTUNet; (**h**) Modified-UNet++; (**i**) Proposed bilateral dissimilarity U-Net. Figure 15: Changed detection maps of the proposed and conventional algorithms for “Test area 2” for the reverse input order: (**a**) Image P; (**b**) Image C; (**c**) Ground truth; (**d**) DI + UNet; (**e**) DI + ATTUNet; (**f**) JF + UNet; (**g**) JF + ATTUNet; (**h**) Modified-UNet++; (**i**) Proposed Bilateral dissimilarity U-Net. Figure 16: Changed detection maps of the proposed and conventional algorithms for “Test area 3”: (**a**) Image P; (**b**) Image C; (**c**) Ground truth; (**d**) DI + UNet; (**e**) DI + ATTUNet; (**f**) JF + UNet; (**g**) JF + ATTUNet; (**h**) Modified-UNet++; (**i**) Proposed bilateral U-Net. Figure 17: Changed detection maps of the proposed and conventional algorithms for “Test area 3” for the reverse input order: (**a**) Image P; (**b**) Image C; (**c**) Ground truth; (**d**) DI + UNet; (**e**) DI + ATTUNet; (**f**) JF + UNet; (**g**) JF + ATTUNet; (**h**) Modified-UNet++; (**i**) Proposed bilateral U-Net. Figure 18 shows the change maps for \"Test area 4.\" In this area, the proposed network visually achieves better change detection than the conventional approaches. The conventional methods with DI result in worse change maps, which imply ineffective use of DI for surface characteristics having very complex structures. They generate more false positives and false negatives. In addition, the use of JF is more effective than the use of DI, resulting in better change maps. However, the proposed network outperforms the conventional methods. For the reverse input order, the proposed algorithm generates a consistent change map, as shown in Figure 12:0 The ATTUNet with JF is significantly degraded, producing many more false positives. Almost all the building regions are detected as changes. In addition, Modified-UNet++ yields a very unclear change map in which almost all regions are identified as changes. Table 2 shows the values of \(F_{1}\)-score and KC to measure the detection accuracy objectively for the proposed and conventional algorithms. For \"Test area 1,\" all the algorithms achieve \(F_{1}\)-score and KC values greater than 0.75 and 75, respectively. In terms of KC interpretation, as tabulated in Table 1, the conventional approaches with DI yield KC score interpreted as \"Good,\" which means that they can identify the change precisely, as shown in Figure 12:1 In addition, the other conventional approaches with JF achieve better scores than when using DI both in terms of \(F_{1}\)-score and KC, interpreted as \"Very Good,\" which means that they can detect changes very well. However, the proposed architecture yields the highest score in terms of \(F_{1}\)-score and KC, 0.88 and 87.11, respectively, which implies that the proposed approach can identify changes more precisely than the conventional methods. For \"Test area 2,\" U-Net with DI still results in the lowest values of \(F_{1}\)-score and KC, 0.55 and 54.37, respectively, which mean \"moderate\" outcomes.