Journal Article

Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning

Liu, Bin, Yang, Chen, Guan, Renchu, Benediktsson, Jon Atli, Zhao, Haishi, Bruzzone, Lorenzo, Ouyang, Ziyuan, Li, Chunlai, Liang, Yanchun, Zeng, Xingguo

2020

Overview

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. Using Chang’E data, the authors here identify more than 109,000 previously unrecognized lunar craters and date almost 19,000 craters based on transfer learning with deep neural networks. A new lunar crater database is derived and distributed to the planetary community.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 704/445/215 / 704/445/849 / Age / Age determination / Artificial neural networks / Chronology / Data analysis / Fossils / Humanities and Social Sciences / Lunar craters / Lunar surface / Machine learning / Moon / multidisciplinary / Neural networks / Science / Science (multidisciplinary) / Solar system / Stratigraphy / Transfer learning