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.

中文翻译：

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生成对抗网络的遥感数据融合：最先进的方法和未来的研究方向

在过去的几十年里，遥感（RS）数据融合一直是一个活跃的研究社区。已经开发了大量的算法和模型。生成对抗网络 (GAN) 作为深度学习的一个重要分支，在各种 RS 图像融合中表现出有希望的表现。本综述介绍了用于 RS 数据融合的 GAN。我们简要回顾了GANs在数据融合中常用的架构和特点，并全面讨论了如何使用GANs实现同构RS、异构RS以及RS与地面观测（GO）数据的融合。我们还分析了基于 GAN 的 RS 图像融合的一些典型应用。这篇综述提供了如何使 GAN 适应不同类型的融合任务的见解，并总结了基于 GAN 的 RS 数据融合的优缺点。最后，