The super-resolution (SR) reconstruction of remote sensing images is a low-cost and efficient method to improve their resolution, and it is often used for further image analysis. To understand the development of SR reconstruction of remote sensing images and research hotspots and trends, we examined its history and reviewed existing methods categorized into traditional, learning-based, and deep-learning-based methods. To evaluate the reconstruction performance, we conducted experiments comparing various algorithms for the single- and multi-frame SR reconstruction of remote sensing images considering three datasets. The experimental results indicate the advantages and limitations of single- and multi-frame reconstruction, with the latter showing a higher performance. Finally, we provide directions for future development of this SR reconstruction.

中文翻译：

---

遥感影像超分辨率重建研究综述

遥感图像的超分辨率（SR）重建是一种低成本、高效的提高其分辨率的方法，常用于进一步的图像分析。为了了解遥感图像SR重建的发展以及研究热点和趋势，我们回顾了其历史并回顾了现有方法，分为传统方法、基于学习的方法和基于深度学习的方法。为了评估重建性能，我们进行了实验，比较了考虑三个数据集的遥感图像单帧和多帧 SR 重建的各种算法。实验结果表明单帧和多帧重建的优点和局限性，后者表现出更高的性能。最后，我们为这种 SR 重建的未来发展提供了方向。