Lunar Crater Detection on Digital Elevation Model: A Complete Workflow Using Deep Learning and Its Application
Impact cratering process is the major geologic activity on the surface of the Moon, and the spatial distribution and size-frequency distribution of lunar craters are indicative to the bombardment history of the Solar System. The substantial efforts on the development of automated crater detection al...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/3/621 |
| _version_ | 1797485049823625216 |
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| author | Xuxin Lin Zhenwei Zhu Xiaoyuan Yu Xiaoyu Ji Tao Luo Xiangyu Xi Menghua Zhu Yanyan Liang |
| author_facet | Xuxin Lin Zhenwei Zhu Xiaoyuan Yu Xiaoyu Ji Tao Luo Xiangyu Xi Menghua Zhu Yanyan Liang |
| author_sort | Xuxin Lin |
| collection | DOAJ |
| description | Impact cratering process is the major geologic activity on the surface of the Moon, and the spatial distribution and size-frequency distribution of lunar craters are indicative to the bombardment history of the Solar System. The substantial efforts on the development of automated crater detection algorithms (CDAs) have been carried out on the images from the remote sensing observations. Recently, CDAs via convolutional neural network (CNN) on digital elevation model (DEM) has been developed as it can combine the discrimination ability of CNN with the robust characteristic of the DEM data. However, most of the existing algorithms adopt a traditional two-stage detection pipeline including an edge segmentation and a template matching step. In this paper, we attempt to reduce the gap between the existing DEM-based CDAs and the advanced CNN methods for object detection, and propose a complete workflow including an end-to-end deep learning pipeline for lunar crater detection, in particular for craters smaller than 50 km in diameter. Based on the workflow, we benchmark nine representative CNN models involving three popular types of detection architectures. Moreover, we elaborate on the practical application of the proposed workflow, and provide an example method to demonstrate the performance advantage in terms of the precision (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>82.97</mn><mo>%</mo></mrow></semantics></math></inline-formula>) and recall (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>79.39</mn><mo>%</mo></mrow></semantics></math></inline-formula>). Furthermore, we develop a crater verification tool to manually validate the detection results, and the visualization results show that our detected craters are reasonable and can be used as a supplement to the existing hand-labeled datasets. |
| first_indexed | 2024-03-09T23:14:15Z |
| format | Article |
| id | doaj.art-f71bdc615cc34361b7fd2172ea6d84d2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T23:14:15Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f71bdc615cc34361b7fd2172ea6d84d22023-11-23T17:40:44ZengMDPI AGRemote Sensing2072-42922022-01-0114362110.3390/rs14030621Lunar Crater Detection on Digital Elevation Model: A Complete Workflow Using Deep Learning and Its ApplicationXuxin Lin0Zhenwei Zhu1Xiaoyuan Yu2Xiaoyu Ji3Tao Luo4Xiangyu Xi5Menghua Zhu6Yanyan Liang7Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau 999078, ChinaFaculty of Information Technology, Macau University of Science and Technology, Taipa, Macau 999078, ChinaFaculty of Information Technology, Macau University of Science and Technology, Taipa, Macau 999078, ChinaFaculty of Information Technology, Macau University of Science and Technology, Taipa, Macau 999078, ChinaFaculty of Information Technology, Macau University of Science and Technology, Taipa, Macau 999078, ChinaFaculty of Information Technology, Macau University of Science and Technology, Taipa, Macau 999078, ChinaFaculty of Information Technology, Macau University of Science and Technology, Taipa, Macau 999078, ChinaFaculty of Information Technology, Macau University of Science and Technology, Taipa, Macau 999078, ChinaImpact cratering process is the major geologic activity on the surface of the Moon, and the spatial distribution and size-frequency distribution of lunar craters are indicative to the bombardment history of the Solar System. The substantial efforts on the development of automated crater detection algorithms (CDAs) have been carried out on the images from the remote sensing observations. Recently, CDAs via convolutional neural network (CNN) on digital elevation model (DEM) has been developed as it can combine the discrimination ability of CNN with the robust characteristic of the DEM data. However, most of the existing algorithms adopt a traditional two-stage detection pipeline including an edge segmentation and a template matching step. In this paper, we attempt to reduce the gap between the existing DEM-based CDAs and the advanced CNN methods for object detection, and propose a complete workflow including an end-to-end deep learning pipeline for lunar crater detection, in particular for craters smaller than 50 km in diameter. Based on the workflow, we benchmark nine representative CNN models involving three popular types of detection architectures. Moreover, we elaborate on the practical application of the proposed workflow, and provide an example method to demonstrate the performance advantage in terms of the precision (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>82.97</mn><mo>%</mo></mrow></semantics></math></inline-formula>) and recall (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>79.39</mn><mo>%</mo></mrow></semantics></math></inline-formula>). Furthermore, we develop a crater verification tool to manually validate the detection results, and the visualization results show that our detected craters are reasonable and can be used as a supplement to the existing hand-labeled datasets.https://www.mdpi.com/2072-4292/14/3/621crater detection algorithm (CDA)digital elevation model (DEM)impact craterdeep learning |
| spellingShingle | Xuxin Lin Zhenwei Zhu Xiaoyuan Yu Xiaoyu Ji Tao Luo Xiangyu Xi Menghua Zhu Yanyan Liang Lunar Crater Detection on Digital Elevation Model: A Complete Workflow Using Deep Learning and Its Application Remote Sensing crater detection algorithm (CDA) digital elevation model (DEM) impact crater deep learning |
| title | Lunar Crater Detection on Digital Elevation Model: A Complete Workflow Using Deep Learning and Its Application |
| title_full | Lunar Crater Detection on Digital Elevation Model: A Complete Workflow Using Deep Learning and Its Application |
| title_fullStr | Lunar Crater Detection on Digital Elevation Model: A Complete Workflow Using Deep Learning and Its Application |
| title_full_unstemmed | Lunar Crater Detection on Digital Elevation Model: A Complete Workflow Using Deep Learning and Its Application |
| title_short | Lunar Crater Detection on Digital Elevation Model: A Complete Workflow Using Deep Learning and Its Application |
| title_sort | lunar crater detection on digital elevation model a complete workflow using deep learning and its application |
| topic | crater detection algorithm (CDA) digital elevation model (DEM) impact crater deep learning |
| url | https://www.mdpi.com/2072-4292/14/3/621 |
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