A Review on Remote Sensing Data Fusion with Generative Adversarial
Networks (GAN)
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 (GAN), as an important
branch of deep learning, show promising performances in variety of RS
image fusions. This review provides an introduction to GAN for remote
sensing data fusion. We briefly review the frequently-used architecture
and characteristics of GAN in data fusion and comprehensively discuss
how to use GAN to realize fusion for homogeneous RS data, heterogeneous
RS data, and RS and ground observation data. We also analyzed some
typical applications with GAN-based RS image fusion. This review takes
insight into how to make GAN adapt to different types of fusion tasks
and summarizes the advantages and disadvantages of GAN-based RS data
fusion. Finally, we discuss the promising future research directions and
make a prediction on its trends.