Remote Sensing Data Fusion With Generative Adversarial Networks: State-of-the-art methods and future research directions

Abstract

In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative adversarial networks (GANs), as an important branch of deep learning, show promising performances in a variety of RS image fusions. This review provides an introduction to GANs for RS data fusion. We briefly review the frequently used architecture and characteristics of GANs in data fusion and comprehensively discuss how to use GANs to realize fusion for homogeneous RS, heterogeneous RS, and RS and ground observation (GO) data. We also analyze some typical applications with GAN-based RS image fusion. This review provides insight into how to make GANs adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss promising future research directions and make a prediction on their trends.