Research progress in super-resolution mapping from remotely sensed imagery
遥感影像超分辨率制图研究进展

Scale problem is one of the central issues in land cover mapping from remote sensing imagery, and downscaling land cover data is a difficulty in this domain. Soft classification can provide more information than hard classification at pixel level. However, the spatial location of land cover compositions within each pixel is unknown. To solve this problem, many super-resolution mapping methods have been developed in recent years. In this paper, a review on recent development of super-resolution mapping methods is presented. The review focuses on the research status, major algorithms and accuracy assessment methods of super-resolution mapping. The main drawbacks, research challenges and future directions of super-resolution mapping are discussed.