Image Translation Between Sar and Optical Imagery with Generative Adversarial Nets

Kenji Enomoto, Ken Sakurada, Weiming Wang, Nobuo Kawaguchi, Masashi Matsuoka, Ryosuke Nakamura

Abstract: In this paper, we propose a method for the translation from Synthetic Aperture Radar (SAR) to optical images using conditional Generative Adversarial Networks (cGANs). Satellite images have been widely utilized for various purposes, such as natural environment monitoring (pollution, forest or rivers), transportation improvement and prompt emergency response to disasters. However, the obscurity caused by clouds leads to unstable monitoring of the ground situation while using the optical camera. Images captured by a longer wavelength are introduced to reduce the effects of clouds. In particular, SAR images are known to be nearly unaffected by clouds and are often used for stably observing the ground situation. On the other hand, SAR images have lower spatial resolution and visibility than optical images. Therefore, we propose a deep neural network that generates optical images from SAR images. Finally, we confirm the feasibility of the proposed network on a dataset consisting of optical images and the corresponding SAR images.