关键词: 遥感图像, 超分辨, 机器学习, 深度学习

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