# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
## 4 Experimental Evaluation and Discussion  
This study used a dataset of panchromatic images, which provided 0.7 GSD captured by the KOMPSAT-3 sensor. For the training dataset, this study used a scene of overlapped images (\(1214\ \times\ 886\)) over Seoul, South Korea, as shown in Figure 6. These images were cropped into a \(40\ \times\ 40\) sliding patch, and the center pixels of the cropped patch pair were labeled based on the ground truth. Figure 6 shows an area containing completed changes and changes under contraction. In addition, these images have many tall buildings, roads, houses, and forests to be trained for solving the misalignment and viewing angle problems. In our experiments, to assess the effectiveness of the proposed change detection system, three areas of the panchromatic datasets were used, namely, Areas 1, 2, and 3, as shown in Figure 7.  
Figure 6: Training dataset: (a) Image acquired in March 2014, (b) image acquired in December 2015, and (c) the ground truth. The images in Figure 7 were acquired in March 2014 and October 2015 over different areas of Seoul, South Korea. Each image pair had been radiometrically corrected and had a geometric misalignment of approximately \(\pm 6\) pixels. In addition, it also had a different angle view, which cannot be resolved without precise 3D building models. Area 1 was located in a downtown part of Seoul, and contained areas changed through building construction. Moreover, the urban area had tall buildings and roads. These datasets included several factors of geometric distortion, misalignments, and different viewing angle effects, which could lead to many false changes. In addition, Area 2 represented a downtown area near a forest. These two images were acquired in different seasons. It was difficult to achieve robustness to seasonal changes for practical applications. Area 3 had many tall buildings, making it difficult to achieve an accurate detection rate owing to the different viewing angles. In this study, the receiver operating characteristic (ROC) curve, AUC, PCC, and Kappa coefficient were used to quantitatively evaluate the performance of the proposed method. Moreover, to evaluate the effectiveness of the proposed method, it was compared with conventional algorithms having FDN and BD-dDCN architectures [28]. A DI and JF were incorporated into a single-path CNN architecture (DI + CNN and JF + CNN). These architectures included eight depth convolutional, two pooling, and two fully connected layers, which were the same as the proposed depth layers. In addition, Dual-DCN [28] was also compared to the proposed method. Figure 8 shows an ROC curve, which indicates that the proposed method could achieve a better AUC compared to the existing algorithms. For Area 1, the proposed method yielded an AUC of 0.9904, which means that it could identify changes approximating the ground truth. It had a slightly higher dual-DCN of 0.9878. The FDN architectures provided an AUC lower than the proposed algorithm  
Figure 7: Experiment dataset: (**a**) Input image for Area 1 (March 2014), (**b**), input image for Area 1 (October 2015), (**c**) ground truth for Area 1, (**d**) input image for Area 2 (March 2014), (**e**) input image for Area 2 (October 2015), (**f**) ground truth for Area 2, (**g**) input image for Area 3, (March 2014), (**h**) image input for Area 3 (October 2015), and (**i**) ground truth for Area 3.  
which JF + CNN and DI + CNN gave an AUC of approximately 0.9509 and 0.7060, respectively. Furthermore, the proposed method significantly outperformed the other algorithms with regard to the AUC for Areas 2 and 3 because it could properly detect the change events with the incorporation of low- and high-level differential features. Table 2 summarizes the PCC and Kappa values of the different methods applied for the three areas. The proposed method showed a higher PCC in Areas 1 and 3. The dual-DCN achieved a slightly higher PCC than the proposed method in Area 2.