# Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery
## 4 Experimental Evaluation and Discussion  
This paper uses a KOMPSAT-3 image dataset that was captured over South Korea. The KOMPSAT-3 image data set is provided by the Korea Aerospace Research Institute. Note that panchromatic band images which provide 0.7 m GSD are used for change detection. Figure 5 shows the example of an overlapped panchromatic images (\(1214\times 886\)) of the training dataset. These images were cropped by \(40\times 40\) sliding patch. The labels for the dataset were manually constructed for all of the center pixels of cropped patch pair. Figure 5: Seoul training data set: (**a**) image acquired in March 2014, (**b**) image acquired in December 2015, and (**c**) ground truth. Figure 6 shows the two panchromatic images of \((29,368\times 27,388)\) and \((29,188\times 28,140)\) used in our experiments, which were acquired by KOMPSAT-3 on March 2014 and October 2015, respectively. These two images were acquired not only at different time instances, but also with different viewing angles. They have geometric misalignment of approximately \(\pm 6\) pixels for overlapped area. Figure 7 shows four selected urban areas from Figure 6, and they contain changed areas due to building construction. In Area 1, there are two types of construction changes, under construction changes and completed construction changes. Moreover, in certain areas, there are tall buildings, which could lead to false changes in change detection due to differing viewing angles. Rather than construction changes and tall buildings, we can find a forest area in Area 2. There are many tall buildings in Area 3, and accurate detection is not easy due to a large different viewing angle. Area 4 is used to assess the influence of change detection due to differing seasons for a forest area. This case is challenging because the change due to the season should be disregarded for practical applications. Note that the labels for four areas were manually obtained, as shown in Figure 7.  
In order to evaluate the change detection performance of the proposed algorithm and conventional algorithms, several metrics are used in this study, including receiver operating characteristic (ROC) curve, area under the curve (AUC), percentage correct classification (PCC), and Kappa coefficient [32]. For existing algorithms, DI + CNN, FF + CNN, and Siamese network were implemented. This CNN architecture includes 8 depth convolutional, 2 pooling, and 2 fully connected layers. For fair comparison, the same parameters of training parameters, the number feature maps, and training dataset were used in our evaluation. Figure 6: PAN images of Seoul area, overlapped area denoted by blue lines. (Left image: March 2014, right image: October 2015). Figure 8 shows detection results for four areas with the exiting algorithms and the proposed algorithm. As shown in Figure 8, the proposed algorithm and FF + CNN generate better detection accuracy for Area 1. On the other hand, DI + CNN and Siamese network produce many false positives for the area. For urban surfaces, it is relatively difficult to handle the misalignment and the different viewing angle impacts because there exists tall buildings and complex constructions, resulting in the fact that false detections are likely to be performed. For Area 2, FF + CNN and DI + CNN yield more false positives, particularly in forest and urban areas. Moreover, Siamese net achieves a better detection result than other conventional algorithms. However, many false positives are still detected in certain areas. Overall, the proposed dual-DCN gives proper change detection performance, even in different viewing angle conditions. For Area 3, the proposed algorithm is still able to properly detect the changes. Figure 7: Four areas of Figure 6. (**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).