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Three-Dimensional Model of the Moon with Semantic Information of Craters Based on Chang’e Data

China’s Chang’e lunar exploration project obtains digital orthophoto image (DOM) and digital elevation model (DEM) data covering the whole Moon, which are critical to lunar research. The DOM data have three resolutions (i.e., 7, 20 and 50 m), while the DEM has two resolutions (i.e., 20 and 50 m). An...

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Autores principales: Lu, Yunfan, Hu, Yifan, Xiao, Jun, Liu, Lupeng, Zhang, Long, Wang, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867113/
https://www.ncbi.nlm.nih.gov/pubmed/33535471
http://dx.doi.org/10.3390/s21030959
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author Lu, Yunfan
Hu, Yifan
Xiao, Jun
Liu, Lupeng
Zhang, Long
Wang, Ying
author_facet Lu, Yunfan
Hu, Yifan
Xiao, Jun
Liu, Lupeng
Zhang, Long
Wang, Ying
author_sort Lu, Yunfan
collection PubMed
description China’s Chang’e lunar exploration project obtains digital orthophoto image (DOM) and digital elevation model (DEM) data covering the whole Moon, which are critical to lunar research. The DOM data have three resolutions (i.e., 7, 20 and 50 m), while the DEM has two resolutions (i.e., 20 and 50 m). Analysis and research on these image data effectively help humans to understand the Moon. In addition, impact craters are considered the most basic feature of the Moon’s surface. Statistics regarding the size and distribution of impact craters are essential for lunar geology. In existing works, however, the lunar surface has been reconstructed less accurately, and there is insufficient semantic information regarding the craters. In order to build a three-dimensional (3D) model of the Moon with crater information using Chang‘e data in the Chang‘e reference frame, we propose a four-step framework. First, software is implemented to annotate the lunar impact craters from Chang’e data by complying with our existing study on an auxiliary annotation method and open-source software LabelMe. Second, auxiliary annotation software is adopted to annotate six segments in the Chang’e data for an overall 25,250 impact crater targets. The existing but inaccurate craters are combined with our labeled data to generate a larger dataset of craters. This data set is analyzed and compared with the common detection data. Third, deep learning detection methods are employed to detect impact craters. To address the problem attributed to the resolution of Chang’e data being too high, a quadtree decomposition is conducted. Lastly, a geographic information system is used to map the DEM data to 3D space and annotate the semantic information of the impact craters. In brief, a 3D model of the Moon with crater information is implemented based on Chang’e data in the Chang‘e reference frame, which is of high significance.
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spelling pubmed-78671132021-02-07 Three-Dimensional Model of the Moon with Semantic Information of Craters Based on Chang’e Data Lu, Yunfan Hu, Yifan Xiao, Jun Liu, Lupeng Zhang, Long Wang, Ying Sensors (Basel) Article China’s Chang’e lunar exploration project obtains digital orthophoto image (DOM) and digital elevation model (DEM) data covering the whole Moon, which are critical to lunar research. The DOM data have three resolutions (i.e., 7, 20 and 50 m), while the DEM has two resolutions (i.e., 20 and 50 m). Analysis and research on these image data effectively help humans to understand the Moon. In addition, impact craters are considered the most basic feature of the Moon’s surface. Statistics regarding the size and distribution of impact craters are essential for lunar geology. In existing works, however, the lunar surface has been reconstructed less accurately, and there is insufficient semantic information regarding the craters. In order to build a three-dimensional (3D) model of the Moon with crater information using Chang‘e data in the Chang‘e reference frame, we propose a four-step framework. First, software is implemented to annotate the lunar impact craters from Chang’e data by complying with our existing study on an auxiliary annotation method and open-source software LabelMe. Second, auxiliary annotation software is adopted to annotate six segments in the Chang’e data for an overall 25,250 impact crater targets. The existing but inaccurate craters are combined with our labeled data to generate a larger dataset of craters. This data set is analyzed and compared with the common detection data. Third, deep learning detection methods are employed to detect impact craters. To address the problem attributed to the resolution of Chang’e data being too high, a quadtree decomposition is conducted. Lastly, a geographic information system is used to map the DEM data to 3D space and annotate the semantic information of the impact craters. In brief, a 3D model of the Moon with crater information is implemented based on Chang’e data in the Chang‘e reference frame, which is of high significance. MDPI 2021-02-01 /pmc/articles/PMC7867113/ /pubmed/33535471 http://dx.doi.org/10.3390/s21030959 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lu, Yunfan
Hu, Yifan
Xiao, Jun
Liu, Lupeng
Zhang, Long
Wang, Ying
Three-Dimensional Model of the Moon with Semantic Information of Craters Based on Chang’e Data
title Three-Dimensional Model of the Moon with Semantic Information of Craters Based on Chang’e Data
title_full Three-Dimensional Model of the Moon with Semantic Information of Craters Based on Chang’e Data
title_fullStr Three-Dimensional Model of the Moon with Semantic Information of Craters Based on Chang’e Data
title_full_unstemmed Three-Dimensional Model of the Moon with Semantic Information of Craters Based on Chang’e Data
title_short Three-Dimensional Model of the Moon with Semantic Information of Craters Based on Chang’e Data
title_sort three-dimensional model of the moon with semantic information of craters based on chang’e data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867113/
https://www.ncbi.nlm.nih.gov/pubmed/33535471
http://dx.doi.org/10.3390/s21030959
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