Generative Adversarial Networks for anomaly detection in aerial images

Generative Adversarial Networks (GANs) are commonly used as a system able to perform unsupervised learning. We propose and demonstrate the use of a GAN architecture, known as the fast Anomaly Generative Adversarial Network (f-AnoGAN), to solve the problem of anomaly detection from aerial images. This architecture was previously applied to medical images and, in this work, we adapt it for use on satellite or aerial photographs. To test the effectiveness of this approach, we implemented anomaly detection schemes based on the Bi-directional Generative Adversarial Network (BiGAN), the image -z -image mapping (izi), the z -image -z (ziz) mapping, and a deep convolutional autoencoder (AE). The results show that the f-AnoGAN outperformed others, achieving AUC (area under the curve) values of 0.99 and 0.92 for urban and rural spaces image sets, respectively.