Abstract:

This paper surveys the research and development of machine learning-based Super-Resolution（SR） reconstruction technique of remote sensing images. The machine learning-based remote sensing image SR reconstruction technique can improve the spatial resolution of remote sensing image by learning the mapping relationship between low resolution image and high resolution image, thus contributing to the visual analysis of remote sensing image. Firstly, according to the difference of data expression methods, machine learning-based SR methods of the remote sensing image are divided into two categories, i.e., dictionary learning-based methods and deep learning-based methods. Then, it briefly describes the concrete problems of various methods, their design ideas and principle are analyzed and summarized; next the advantages and disadvantages of various methods and reconstruction indicators are compared and analyzed. Finally, the problems and difficulties of remote sensing image SR are summarized and the future development trend of remote sensing image SR is prospected.

Key words: remote sensing image, super-resolution, machine learning, deep learning

摘要：

综述了基于机器学习的遥感图像超分辨重建技术的研究和发展。基于机器学习的遥感图像超分辨率重建技术通过学习低分辨图像与高分辨图像之间映射的关系，提升遥感图像的空间分辨率，从而有助于遥感图像的视觉分析。根据数据表达方法的不同将基于机器学习的遥感图像超分辨方法分为两类，包括基于字典学习的方法和基于深度学习的方法；简述了各类方法针对的问题，分析其设计思路和实现原理；对各类方法的优缺点和性能指标进行了对比分析；总结了遥感图像超分辨面临的问题和难点，并对未来发展的趋势进行了展望。

关键词: 遥感图像, 超分辨, 机器学习, 深度学习