## 4 Experimental Results and Analysis
### Performance Evaluation on the KOMPSAT-3 Dataset
The other conventional approaches can produce a change map interpreted as \"Good,\" achieving \(F_{1}\)-score above 0.61 and KC between 61 and 80, respectively. In this area, the proposed algorithm can identify the changes by achieving an \(F_{1}\)-score over 0.83 and a KC of 82.8, which implies a \"Very Good\" level and better detection than the other conventional approaches. For \"Test area 3,\"  
Figure 12:2 Changed detection maps of the proposed and conventional algorithms for “Test area 4”: (**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. Modified-UNet++ results in \"Fair\" detection. In this area, ATTUNet yields the highest score by achieving an \(F_{1}\)-score of 0.63 and a KC of 62.87, resulting in a \"Good\" interpretation. Note that the proposed algorithm has a slightly worse score than ATTUNet for this case. However, the proposed architecture results in the highest values of \(F_{1}\)-score and KC, 0.40 and 39.78, respectively, for \"Test area 4.\" The conventional approaches achieve \(F_{1}\)-score and KC below 0.32 and 31, respectively. Overall, the proposed method yields better detection in terms of \(F_{1}\)-score and KC on average by achieving 0.68 and 66.93, respectively, which implies a \"Good\" detection rate for change detection. In addition, in terms of \(F_{1}\)-score and KC of the proposed method, the standard deviations are 0.19 and 21.11, indicating relatively stable change detection performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Area** & **Algorithm** & \(F_{1}\)**-Score** & **KC (\%)** \\ \hline \multirow{6}{*}{Test area 1} & DI + UNET & 0.76 & 75.32 \\  & DI + ATTUNET & 0.77 & 75.57 \\  & JF + UNET & 0.83 & 82.82 \\  & JF + ATTUNET & 0.83 & 81.79 \\  & Modified-UNet++ & 0.87 & 86.88 \\  & Proposed & 0.88 & 87.11 \\ \hline \multirow{6}{*}{Test area 2} & DI + UNET & 0.55 & 54.37 \\  & DI + ATTUNET & 0.75 & 74.70 \\ \cline{1-1}  & JF + UNET & 0.75 & 74.41 \\ \cline{1-1}  & JF + ATTUNET & 0.69 & 69.34 \\ \cline{1-1}  & Modified-UNet++ & 0.76 & 75.73 \\ \cline{1-1}  & Proposed & 0.83 & 82.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(F_{1}\)-score and KC of the conventional and proposed algorithms for the KOMPSAT-3 dataset. Figure 19: Changed detection maps of the proposed and conventional algorithm for “Test area 4” in the reversed image 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. Table 3 shows the objective evaluation for the proposed and conventional approaches for reverse image input order. Conventional approaches using JF present significant performance degradation compared to the situation before reversing the image order. It seems that the trained networks only solve the change detection with the same image order as the training setting. In contrast to the conventional methods using DI, they can consistently produce the same result as before reversing the image order. However, the use of DI frequently results in poor detection or lower performance caused by spectral information loss. Overall, the proposed architecture can yield consistent performance with a higher detection rate, even for the reverse image order.