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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.
Liu et al. (2021) studied this question.