Automatic Mapping of Small Lunar Impact Craters Using LRO-NAC Images
Impact craters are the most common feature on the Moon’s surface. Crater size–frequency distributions provide critical insight into the timing of geological events, surface erosion rates, and impact fluxes. The impact crater size–frequency follows a power law (meter-sized craters are a few orders of...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | English |
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AMER GEOPHYSICAL UNION
2022
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| Online Access: | http://purl.org/au-research/grants/arc/DP210100336 http://hdl.handle.net/20.500.11937/94370 |
| _version_ | 1848765862309265408 |
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| author | Fairweather, John Lagain, Anthony Servis, K. Benedix, Gretchen Kumar, S.S. Bland, Phil |
| author_facet | Fairweather, John Lagain, Anthony Servis, K. Benedix, Gretchen Kumar, S.S. Bland, Phil |
| author_sort | Fairweather, John |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Impact craters are the most common feature on the Moon’s surface. Crater size–frequency distributions provide critical insight into the timing of geological events, surface erosion rates, and impact fluxes. The impact crater size–frequency follows a power law (meter-sized craters are a few orders of magnitude more numerous than kilometric ones), making it tedious to manually measure all the craters within an area to the smallest sizes. We can bridge this gap by using a machine learning algorithm. We adapted a Crater Detection Algorithm to work on the highest resolution lunar image data set (Lunar Reconnaissance Orbiter-Narrow-Angle Camera [NAC] images). We describe the retraining and application of the detection model to preprocessed NAC images and discussed the accuracy of the resulting crater detections. We evaluated the model by assessing the results across six NAC images, each covering a different lunar area at differing lighting conditions. We present the model’s average true positive rate for small impact craters (down to 20 m in diameter) is 93%. The model does display a 15% overestimation in calculated crater diameters. The presented crater detection model shows acceptable performance on NAC images with incidence angles ranging between ∼50° and ∼70° and can be applied to many lunar sites independent to morphology. |
| first_indexed | 2025-11-14T11:41:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-94370 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:41:59Z |
| publishDate | 2022 |
| publisher | AMER GEOPHYSICAL UNION |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-943702024-04-04T07:27:44Z Automatic Mapping of Small Lunar Impact Craters Using LRO-NAC Images Fairweather, John Lagain, Anthony Servis, K. Benedix, Gretchen Kumar, S.S. Bland, Phil Science & Technology Physical Sciences Astronomy & Astrophysics Geosciences, Multidisciplinary Geology impact craters Moon Crater Detection Algorithm SIZE-FREQUENCY DISTRIBUTION FRONT FACE BASINS ABSOLUTE AGES DETECTION ALGORITHMS GLOBAL DATABASE HIGH-RESOLUTION IDENTIFICATION TOPOGRAPHY EQUILIBRIUM CONSTRAINTS Impact craters are the most common feature on the Moon’s surface. Crater size–frequency distributions provide critical insight into the timing of geological events, surface erosion rates, and impact fluxes. The impact crater size–frequency follows a power law (meter-sized craters are a few orders of magnitude more numerous than kilometric ones), making it tedious to manually measure all the craters within an area to the smallest sizes. We can bridge this gap by using a machine learning algorithm. We adapted a Crater Detection Algorithm to work on the highest resolution lunar image data set (Lunar Reconnaissance Orbiter-Narrow-Angle Camera [NAC] images). We describe the retraining and application of the detection model to preprocessed NAC images and discussed the accuracy of the resulting crater detections. We evaluated the model by assessing the results across six NAC images, each covering a different lunar area at differing lighting conditions. We present the model’s average true positive rate for small impact craters (down to 20 m in diameter) is 93%. The model does display a 15% overestimation in calculated crater diameters. The presented crater detection model shows acceptable performance on NAC images with incidence angles ranging between ∼50° and ∼70° and can be applied to many lunar sites independent to morphology. 2022 Journal Article http://hdl.handle.net/20.500.11937/94370 10.1029/2021EA002177 English http://purl.org/au-research/grants/arc/DP210100336 http://purl.org/au-research/grants/arc/FT170100024 AMER GEOPHYSICAL UNION fulltext |
| spellingShingle | Science & Technology Physical Sciences Astronomy & Astrophysics Geosciences, Multidisciplinary Geology impact craters Moon Crater Detection Algorithm SIZE-FREQUENCY DISTRIBUTION FRONT FACE BASINS ABSOLUTE AGES DETECTION ALGORITHMS GLOBAL DATABASE HIGH-RESOLUTION IDENTIFICATION TOPOGRAPHY EQUILIBRIUM CONSTRAINTS Fairweather, John Lagain, Anthony Servis, K. Benedix, Gretchen Kumar, S.S. Bland, Phil Automatic Mapping of Small Lunar Impact Craters Using LRO-NAC Images |
| title | Automatic Mapping of Small Lunar Impact Craters Using LRO-NAC Images |
| title_full | Automatic Mapping of Small Lunar Impact Craters Using LRO-NAC Images |
| title_fullStr | Automatic Mapping of Small Lunar Impact Craters Using LRO-NAC Images |
| title_full_unstemmed | Automatic Mapping of Small Lunar Impact Craters Using LRO-NAC Images |
| title_short | Automatic Mapping of Small Lunar Impact Craters Using LRO-NAC Images |
| title_sort | automatic mapping of small lunar impact craters using lro-nac images |
| topic | Science & Technology Physical Sciences Astronomy & Astrophysics Geosciences, Multidisciplinary Geology impact craters Moon Crater Detection Algorithm SIZE-FREQUENCY DISTRIBUTION FRONT FACE BASINS ABSOLUTE AGES DETECTION ALGORITHMS GLOBAL DATABASE HIGH-RESOLUTION IDENTIFICATION TOPOGRAPHY EQUILIBRIUM CONSTRAINTS |
| url | http://purl.org/au-research/grants/arc/DP210100336 http://purl.org/au-research/grants/arc/DP210100336 http://hdl.handle.net/20.500.11937/94370 |