Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning
Year: 2020
Type: article
Abstract: Impact craters, which can be considered the lunar equivalent of fossils, are the most dominant lunar surface features and record the history of the Solar System. We address the problem of automatic crater detection and age estimation. From initially small numbers of recognized craters and dated craters, i.e., 7895 and 1411, respectively, we progressively identify new craters and estimate their ages with Chang'E data and stratigraphic information by transfer learning using deep neural networks. This results in the identification of 109,956 new craters, which is more than a dozen times greater than the initial number of recognized craters. The formation systems of 18,996 newly detected craters larger than 8 km are estimated. Here, a new lunar crater database for the mid- and low-latitude regions of the Moon is derived and distributed to the planetary community together with the related data analysis.
Source: Nature Communications
Authors: Chen Yang, Haishi Zhao, Lorenzo Bruzzone, Jon Atli Benediktsson, Yanchun Liang, Bin Liu, Xingguo Zeng, Renchu Guan, Chunlai Li, Ziyuan Ouyang
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $5,380
Awards: 永恒语言学习关键算法研究, 基于深度学习的个性化基因组与药物相互作用研究
OpenAlex ID: W2993800129