Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in Synthetic Aperture Radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. In this paper, we provide an introduction to the most relevant deep learning models and concepts, point out possible pitfalls by analyzing special characteristics of SAR data, review the state-of-the-art of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.

翻译：遥感的深层学习已成为一种国际杂交,但主要限于光学数据的评估。尽管在合成孔径雷达(SAR)数据处理中已经引入了深层学习,尽管第一次尝试成功,但其巨大潜力仍然被锁定。在本文件中,我们介绍了最相关的深层学习模式和概念,通过分析合成孔径雷达数据的特殊性、审查深入应用于合成孔径雷达的深层学习的最新水平、总结现有基准并推荐一些重要的未来研究方向,指出了可能的陷阱。通过这一努力,我们希望刺激在这个令人感兴趣的、但利用不足的研究领域开展更多的研究,并为在大型合成孔径雷达数据处理工作流程中使用深层学习铺平道路。