## 4 Experimental Results and Analysis
### Performance Evaluation on the KOMPSAT-3 Dataset
\begin{table}
\begin{tabular}{c c c c} \hline
**Study Area** & **Algorithm** & \(\mathbf{F_{1}}\)**-Score** & **KC (\%)** \\ \hline \multirow{6}{*}{Test area 3} & DI + UNET & 0.47 & 44.86 \\  & DI + ATTUNET & 0.45 & 42.86 \\  & JF + UNET & 0.54 & 52.69 \\  & JF + ATTUNET & 0.63 & 62.87 \\  & Modified-UNet++ & 0.39 & 36.57 \\  & Proposed & 0.59 & 58.04 \\ \hline \multirow{6}{*}{Test area 4} & DI + UNET & 0.29 & 28.30 \\  & DI + ATTUNET & 0.32 & 31.37 \\  & JF + UNET & 0.28 & 27.74 \\  & JF + ATTUNET & 0.28 & 27.32 \\  & Modified-UNet++ & 0.32 & 31.20 \\  & Proposed & 0.40 & 39.78 \\ \hline \multirow{6}{*}{Avg. \(\pm\) Std.} & DI + UNET & 0.52 \(\pm\) 0.17 & 50.71 \(\pm\) 17.00 \\  & DI + ATTUNET & 0.57 \(\pm\) 0.19 & 56.12 \(\pm\) 19.44 \\ \cline{1-1}  & JF + UNET & 0.60 \(\pm\) 0.21 & 59.41 \(\pm\) 21.34 \\ \cline{1-1}  & JF + ATTUNET & 0.61 \(\pm\) 0.20 & 60.33 \(\pm\) 20.24 \\ \cline{1-1}  & Modified-UNet++ & 0.59 \(\pm\) 0.23 & 57.59 \(\pm\) 24.11 \\ \cline{1-1}  & Proposed & 0.68 \(\pm\) 0.19 & 66.93 \(\pm\) 19.20 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline
**Study Area** & **Algorithm** & \(\mathbf{F_{1}}\)**-Score** & **KC (\%)** \\ \hline \multirow{6}{*}{Test area 1} & DI + UNET & 0.76 & 75.32 \\  & DI + ATTUNET & 0.77 & 75.57 \\  & JF + UNET & 0.35 & 32.9 \\  & JF + ATTUNET & 0.48 & 46.03 \\  & Modified-UNet++ & 0.67 & 65.15 \\  & Proposed & 0.88 & 88.16 \\ \hline \multirow{6}{*}{Test area 2} & DI + UNET & 0.55 & 54.37 \\  & DI + ATTUNET & 0.75 & 74.7 \\ \cline{1-1}  & JF + UNET & 0.42 & 41.78 \\ \cline{1-1}  & JF + ATTUNET & 0.43 & 42.28 \\ \cline{1-1}  & Modified-UNet++ & 0.49 & 49.23 \\ \cline{1-1}  & Proposed & 0.83 & 82.8 \\ \hline \multirow{6}{*}{Test area 3} & DI + UNET & 0.47 & 44.86 \\ \cline{1-1}  & DI + ATTUNET & 0.45 & 42.86 \\ \cline{1-1}  & JF + UNET & 0.12 & 8.12 \\ \cline{1-1}  & JF + ATTUNET & 0.28 & 24.52 \\ \cline{1-1}  & Modified-UNet++ & 0.17 & 11.9 \\ \cline{1-1}  & Proposed & 0.59 & 58.04 \\ \hline \end{tabular}
\end{table} 
Table 3: \(F_{1}\)-score and KC with reverse image order for the KOMPSAT-3 dataset. To investigate the effectiveness of several existing architectures (U-Net [23], ATTUNet [24], and Modified-UNet++ [22]), they were trained with the reversed as well as forward input pair images like data augmentation methods. Table 4 shows \(F_{1}\)-score and KC to objectively measure the detection accuracy for the proposed and existing algorithms for forward input pairs from the data-augmented KOMPSAT-3 dataset. Since the change detection method using DI feature as an input is input-order independent, we only evaluated the existing approaches using JF. The table shows that the existing methods yield higher accuracy on average compared to no data argumentation. In particular, we found that the \(F_{1}\)-score and KC for 'Area 4' of Modified-UNet++ improved the most. However, the proposed architecture outperforms the existing works on average for all the test datasets. Table 5 shows detection accuracy of the proposed and existing algorithms for reversed input order with data-augmented training pair images from the KOMPSAT-3 dataset. While the existing methods are improved in the detection accuracy over those shown in Table 3, the proposed architecture yields better performance on average than the existing methods.