# Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery
## 4 Experimental Evaluation and Discussion
(**h**) Image input for Area 3 (October 2015). (**i**) Ground truth for Area 3. (**j**) Input image for Area 4 (March 2014) (**k**) Input image for Area 4 (October 2015). (**l**) Ground truth for Area 4. The other algorithms result in more false positives. Note that input images for Area 4 were acquired in difference seasons for a forest area. For the test data, Siamese net produces some false positives. As shown in Figure 8, the proposed algorithm yields a better detection result with the proposed dual-DCN. The proposed algorithm can alleviate the impacts of distortions caused by imperfect geometric correction and different viewing angles. As mentioned previously, the proposed dual-DCN was designed to learn the dissimilarity of two local images in order to avoid false changes. That is why the false positive rate is relatively lower by the proposed algorithm. In contrast, DI + CNN and FF + CNN yield higher false positive rates, particularly for Areas 2 and 3. Moreover, the Siamese network produces higher false positives in Area 1, due to less optimized parameters. Figure 9 shows that the proposed algorithm can give better ROC than the conventional algorithms. According to ROC curves, the proposed dual-DCN shows better quantitative detection performance in AUC of 0.97, on average, as tabulated in Table 1. FF + CNN is slightly better in AUC than the proposed dual-DCN for Area 2, because it has better true positive for this case. However, the proposed algorithm has a lower false positive rate than FF + CNN. Table 1 summarizes the PCC and Kappa values of different methods for the three areas. As shown in Table 1, the proposed algorithm achieves higher PCC and Kappa values. We can say that the proposed dual dense convolutional network architecture has the ability to identify both changed and unchanged areas by disregarding irrelevant variations and false changes, even in cases of complicated urban surfaces, geometric distortion, and different viewing angles. Figure 8: Detection results for four areas with the existing algorithms and proposed algorithm. (**a**) FF+ CNN, (**b**) DI + CNN, (**c**) Siamese network, and (**d**) the proposed dual-DCN. Regarding time complexity, the proposed DCN requires more computational complexity than the single architecture using FF + CNN and DI + CNN by a factor of approximately two with sequential machines. However, the proposed dual-DCN can work in parallel, thus, throughput can be enhanced with a parallel machine such as GPU. In addition, the proposed also takes about 20% more running time than the Siamese network because it includes additional preceding of feature maps. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **Algorithms** & **Area 1** & **Area 2** & **Area 3** & **Avg** \\ \hline \multirow{4}{*}{AUC} & FF + CNN & 0.95 & 0.95 & 0.98 & 0.96 \\  & DI + CNN & 0.70 & 0.68 & 0.88 & 0.75 \\  & Siamese net & 0.96 & 0.92 & 0.91 & 0.93 \\  & The proposed & 0.99 & 0.93 & 0.99 & 0.97 \\ \hline \multirow{4}{*}{PCC (\%)} & FF + CNN & 97 & 92 & 98 & 96 \\  & DI + CNN & 94 & 97 & 97 & 96 \\  & Siamese net & 96 & 98 & 99 & 98 \\  & The proposed & 98 & 99 & 99 & 99 \\ \hline \multirow{4}{*}{Kappa} & FF + CNN & 78 & 19 & 47 & 48 \\  & DI + CNN & 30 & 32 & 28 & 30 \\  & Siamese net & 52 & 35 & 68 & 52 \\  & The proposed & 78 & 60 & 69 & 69 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative assessments of the existing and proposed algorithms. Figure 9: ROC for three areas. (**a**) ROC for Area 1, (**b**) ROC for area 2, and (**c**) ROC for Area 3.