Tokyo Metropolitan University
Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)(ISAS)
Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)(ISAS)
Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)(ISAS)
Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)(ISAS)
Gunma University
Okayama University of Science
Tokyo Metropolitan University
出版者
宇宙航空研究開発機構(JAXA)
出版者（英）
Japan Aerospace Exploration Agency (JAXA)
雑誌名
宇宙航空研究開発機構研究開発報告: 宇宙科学情報解析論文誌: 第9号
雑誌名(英)
JAXA Research and Development Report: Journal of Space Science Informatics Japan: Volume 9
Digital Elevation Model (DEM) of the lunar surface is one of the data used to discuss landing sites and travel route candidates for the lunar probe. When using DEM, if the DEM resolution is low, cannot grasp the detail for the terrain. However, the creation of a high-resolution DEM of the lunar surface is expensive because it requires manual work. In this paper, we aim to generate a high-resolution DEM without manual work by using a low-resolution DEM that exists for the entire lunar. In general, the interpolation method is used to generate a higher resolution DEM from the existing DEM. However, the interpolation method is not sufficient for use to discuss the landing site of the lunar explorer and moving path candidates. In this paper, we consider that a method using deep learning that achieves high performance in image super-resolution can be applied and verified the effectiveness of the method. In the evaluation based on the mean of mean error and the mean of maximum error, the verified method generated a DEM with higher accuracy than the general interpolation method. However, we consider that the performance is insufficient for discussing the landing site of the lunar probe and travel route candidates for the lunar probe.
内容記述
形態: カラー図版あり
内容記述（英）
Physical characteristics: Original contains color illustrations
機械学習による月面DEMの高解像化
AA1930025003.pdf
(4.07MB)
