Use of GAN to Help Networks to Detect Urban Change Accurately

Abstract

Mastering urban change information is of great importance and significance in practical areas such as urban development planning, land management, and vegetation cover. At present, high-resolution remote sensing images and deep learning techniques have been widely used in the detection of urban information changes. However, most of the existing change detection networks are Siamese networks based on encoder–decoder architectures, which tend to ignore the pixel-to-pixel relationships and affect the change detection results. To solve this problem, we introduced a generative adversarial network (GAN). The change detection network based on the encoder–decoder architecture was used as the generator of the GAN, and the Jensen-Shannon(JS) scatter in the GAN model was replaced by the Wasserstein distance. An urban scene change detection dataset named XI’AN-CDD was produced to verify the effectiveness of the algorithm. Compared with the baseline model of the change detection network, our generator outperformed it significantly and had higher feature integrity. When the GAN was added, the detected feature integrity was better, and the F1-score increased by 4.4%.