## 4 Experimental Results and Analysis
### Performance Evaluation on the KOMPSAT-3 Dataset  
Figure 12 shows the detection results of the proposed and conventional methods for \"Test area 1.\" In this experiment, the image order was set to the same order as in the training configuration, in which 'Image P' shows an under-construction area, and 'Image C' shows a completed construction area. As shown in Figure 12, the proposed network subjectively yields a more accurate change map compared to the conventional approaches. The conventional U-Net and attention U-Net using DI generate more false positives than when using JF, owing to spectral information loss. JF can preserve the spectral information, which is the reflection characteristic of Earth's surface; thus, the model can learn a better representation of changes. In addition, the proposed network consistently produces a better change map for reverse input order. Figure 13 depicts the detection results for the reverse input order, which can be interpreted as the destruction change case. Conventional methods, such as U-Net, ATTUNet, and Modified-UNet++ using JF, yield worse change maps compared to the detection outcomes for the original order. These conventional approaches are significantly influenced by the input order established for change detection. However, the proposed algorithm consistently outperforms the conventional methods in terms of change detection, regardless of the input order. \begin{table}
\begin{tabular}{c c} \hline \hline
**KC Score (\%)** & **Strength of Agreement** \\ \hline \(<\)20 & Poor \\
21 to 40 & Fair \\
41 to 60 & Moderate \\
61 to 80 & Good \\
81 to 100 & Very Good \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Interpretation of kappa coefficient (KC) score. Figure 12: Changed detection maps of the proposed and conventional algorithms for “Test area 1”: (**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 shows the detection results of the proposed and conventional methods for \"Test area 2,\" in which the input order was the same as that of the training configuration. Conventional studies yield moderate precise change maps. However, U-Nets generate more false positives; in particular, U-Net with DI results in the worst case. ATTUNet and Modified-UNet++ consistently achieve precise change maps using both DI and JF because of their similar surface characteristics with respect to the training dataset. However, the proposed change map visually yields a more accurate change map than other conventional approaches. In the reverse input order, the conventional methods with JF produce worse change maps than those for the original input order, as shown in Figure 15. Their detection outcomes are significantly influenced by the input order. This means that JF is influenced by the image input order when it comes to representing the visual characteristics. Networks with DI can generate relatively consistent change maps, regardless of the reverse input order. In addition, the proposed network consistently maintains a change map regardless of the reverse image order. In \"Test area 3,\" ATTUNet with JF results in a better change map than that of other networks, as shown in Figure 16. We found that the proposed network cannot identify the changes in some regions. This is due to the surface characteristics of this area, which has a more complex surface. Although the proposed algorithm is worse than ATTUNet for this test case, the proposed algorithm can maintain low false positives. Furthermore, the proposed method still outperforms the conventional approaches with DI and Modified-UNet++. Conventional methods with DI cannot maintain high true positives in some regions. Modified-UNet++ also generates false positives in all the building regions.