Visual Prediction of Typhoon Clouds With Hierarchical Generative Adversarial Networks

Abstract

We develop a hierarchical generative adversarial network (HGAN) for generating future typhoon cloud remote sensing images, which enables a visual means to typhoon cloud prediction. The HGAN consists of a global generator and a local discriminator. The global generator aims at producing the future typhoon cloud images as realistic as possible and accordingly reveals the structure and future location of the typhoon clouds. It is constructed in terms of a hierarchical architecture with multiple subnetworks, which capture the overall typhoon variations and favor generating clear future typhoon cloud images. The local discriminator tries its best to distinguish generated typhoon cloud images from ground-truth ones, based on the local patches. The local procedure encourages the discriminator to focus on characterizing the moving typhoon clouds rather than the still background. The global generator and the local discriminator are trained in an adversarial fashion with respect to historical typhoon cloud image sequences. The trained HGAN is capable of producing reliable visual predictions that are not only enabled by the global generator and but also examined by the local discriminator. Experiments validate the effectiveness of the HGAN for typhoon cloud prediction.