## 4 Experimental Results and Analysis  
In this study, the KOMPSAT-3 imagery with product level 1G and aerial change detection datasets were used to assess the effectiveness of the proposed method. The KOMPSAT-3 sensors can capture a panchromatic (PAN, 450-900 nm) image characterized by the high resolution of 0.7 m and multi-spectral (MS) images like blue (B, 450-520 nm), green (G, 520-600 nm), red (R, 630-690 nm), and near-infrared (NIR, 760-900 nm) with the lower spatial resolution of 2.8 m. In these images, pre-processing has been done, such as the radiometric correction, atmosphere errors, and geometric correction. The radiometric correction converts the image pixel values (Digital Numbers/DNs) to surface reflectance values. It involves the conversion of DNs to a radiance value, and then to top-of-atmosphere (TOA) radiance. On the other hand, gain and offset values are provided by the KOMPSAT-3 specification [28] to derive the TOA reflectance values. After the atmosphere errors are corrected, the geometric correction is performed. This study defines changes as the result of construction or destruction activities; natural seasonal changes are excluded. The KOMSAT-3 dataset provides MS and PAN with resolutions of 7342 \(\times\) 6847 \(\times\) 4 and 29,368 \(\times\) 27,388 \(\times\) 1, respectively. The image data used were captured in Seoul city. For our experiments, the multispectral images were enhanced by employing IHS mean-filter pansharpening [13] to produce high-resolution R, G, B, NIR, and PAN channels. Five cropped pairs of images were used for the dataset, and different areas were selected with diverse surface characteristics. Figure 6 shows one set of the training images and the ground truth with a size of 3232 \(\times\) 2206, acquired in March 2014 and December 2015. Note that the images contain multiple construction changes, residential districts, roads, playgrounds, and a small hill in the urban area. For the training setting, the images were divided into multiple patches with a size of 256 \(\times\) 256 by a raster-scan sliding window with a stride of 70, generating 1024 pairs of patches and their ground truths. For the training, this study applies binary cross-entropy loss function and Adam optimizer with training parameters, including 30, 0.0001, 0.9, 0.999, and 4 as number of epochs, learning rate, 81, 82, and batch size, respectively. The proposed and conventional methods were trained over computer spec of Intel i7-6700 CPU @ 3.4 Hz(8 CPUs) with NVIDIA GeForce GTX 1080 Ti GPU. It was implemented over PyTorch. Figure 5: Example of independent difference feature maps on average for \(CT_{1}\) and \(CT_{2}\). To evaluate the effectiveness of the proposed change detection system, four different areas were selected. Figure 7 shows \"Test area 1,\" which has under-construction changes, acquired in March 2014 and December 2015. This area is in downtown Seoul, which has tall buildings, residential districts, playgrounds, and roads. Figure 8 shows \"Test area 2,\" acquired in March 2014 and October 2015, which was also acquired in an urban area with building construction. Figure 9 shows \"Test area 3,\" acquired in March 2014 and October 2015, which also has construction taking place in a forested area. The two images have significant radiometric differences. In addition, \"Test area 4,\" located near a river, was acquired in March 2014 and October 2015 (Figure 10). Note that the image data have one bridge on a river for which geometric distortion occurs owing to different viewpoints. The image data for each test area were divided into 117 patches with a size of 256 \(\times\) 256 and no overlap. All networks were tested for the patch pair. All ground truths were manually generated. The aerial building change detection dataset (CDD) is an open dataset with various resolutions from 3 cm to 100 cm per pixel, and varying times and seasons among the bi-temporal images [29]. The performance of the proposed method was also evaluated for the CDD dataset.