# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
## 4 Experimental Evaluation and Discussion
However, in terms of the Kappa value, the proposed fusion network outperformed all other existing algorithms. The proposed method achieved a Kappa value of 75.16 on average, which means that it yielded a good agreement in terms of the results. Figure 9 shows the change map results when applying the existing and proposed algorithms. Visually, the proposed method achieved a much better result than the existing algorithms. In Area 1, the proposed fusion network nearly approximated the ground truth. It could reduce the number of false positives while preserving the true positives. The proposed network produced a cleaner change map than the existing algorithms regarding false positives. Moreover, the proposed algorithm yielded reasonably good results for Areas 2 and 3. The proposed method significantly reduced the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Area 1**} & \multicolumn{3}{c}{**Area 2**} & \multicolumn{3}{c}{**Area 3**} \\ \cline{2-10}  & **AUC** & **PCC** & **Kappa** & **AUC** & **PCC** & **Kappa** & **AUC** & **PCC** & **Kappa** \\ \hline DI + CNN & 0.7060 & 0.9458 & 36.8938 & 0.6764 & 0.9571 & 11.8939 & 0.7213 & 0.9855 & 33.2651 \\ JF + CNN & 0.9509 & 0.9775 & 79.7190 & 0.9536 & 0.9570 & 29.7251 & 0.7847 & 0.9732 & 47.6066 \\ Dual-DCN & 0.9878 & 0.9774 & 78.4277 & 0.9546 & 0.9922 & 60.0070 & 0.8515 & 0.9751 & 50.7542 \\ Proposed & 0.9904 & 0.9782 & 80.7942 & 0.9707 & 0.9902 & 65.9929 & 0.9517 & 0.9892 & 78.6898 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative assessment of the existing and proposed algorithms. Figure 8: Receiver operating characteristic (ROC) for (**a**) Area 1, (**b**) Area 2, and (**c**) Area 3.  
number of false positives and enhanced the true positives. This is caused by the proposed fusion network, which was designed and trained for low- and high-level differential problems. In addition, a post-processing step was employed to validate and repair the change map. To evaluate the effectiveness of the proposed two-stage decision, the proposed algorithm also was compared to each individual network output (\(D\) and \(O\)) and the other decision method between two outputs of the proposed fusion network based on the mean operation. In addition, another single output fusion network (SOFN) architecture was designed same as the proposed fusion network architecture by fusing \(D\) and \(O\) outputs for more comparisons. This network was trained with the binary cross entropy loss function by the same training parameters. The objective and subjective evaluation are presented in Table 3 and Figure 10, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{3}{c}{**Area 1**} & \multicolumn{3}{c}{**Area 2**} & \multicolumn{3}{c}{**Area 3**} \\ \cline{2-10}  & **AUC** & **PCC** & **Kappa** & **AUC** & **PCC** & **Kappa** & **AUC** & **PCC** & **Kappa** \\ \hline D & 0.9206 & 0.9655 & 65.3497 & 0.8154 & 0.9854 & 33.8273 & 0.8410 & 0.9794 & 55.1115 \\ O & 0.9357 & 0.9607 & 61.9808 & 0.8948 & 0.9879 & 50.0476 & 0.8667 & 0.9436 & 31.3879 \\ Mean & 0.9886 & 0.9781 & 78.4481 & 0.9588 & 0.9875 & 52.7712 & 0.9165 & 0.9803 & 59.5712 \\ SOFN & 0.9685 & 0.9661 & 65.7595 & 0.8903 & 0.9897 & 52.6660 & 0.8658 & 0.9820 & 61.2094 \\ Proposed & 0.9904 & 0.9782 & 80.7942 & 0.9707 & 0.9902 & 65.9929 & 0.9517 & 0.9892 & 78.6898 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative assessment of single output decision and proposed algorithms. Figure 9: Detection results for three areas when using the existing and proposed algorithms: (**a**) DI + CNN, (**b**) JF + CNN, (**c**) dual-DCN, and (**d**) the proposed fusion network. According to Table 3, the proposed two-stage decision shows better performance compared to individual outputs and mean operation. In term of AUC, PCC, and Kappa, the proposed gave significantly better results than that by individual outputs (\(D\) and \(O\)).