GAN-Based Satellite Imaging: A Survey on Techniques and Applications

Abstract

Satellite image analysis is widely used in many real-time applications, from agriculture to the military. Due to the wide range of Generative Adversarial Network (GAN) applications in multiple areas of satellite imaging, a comprehensive review is required in this area. This paper takes the first step in this direction by categorizing the GAN-based satellite imaging research using seven considerations. We discuss not only the challenges but also future research trends and directions. Among the major findings, we have observed increasing componentization and modularization of GANs to be used as elements of larger systems. In addition to the GAN types used exclusively in each application, we demonstrate the deep neural network architectures used as the generator structure. Eventually, we summarize the results and evaluate the significant impact of GANs on improving performance compared to traditional approaches.