Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin
We apply machine learning techniques to identify and map resurfacing units in the central South Pole−Aitken (SPA) basin using three Lunar Reconnaissance Orbiter (LRO) mission data sets: 321/415 nm and 566/689 nm band reflectance ratios from Hapke photometrically standardized albedo maps and a Terrai...
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2025-01-01
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| Online Access: | https://doi.org/10.3847/PSJ/ada4a6 |
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| author | Frank C. Chuang Matthew D. Richardson Jennifer L. Whitten Daniel P. Moriarty Deborah L. Domingue |
| author_facet | Frank C. Chuang Matthew D. Richardson Jennifer L. Whitten Daniel P. Moriarty Deborah L. Domingue |
| author_sort | Frank C. Chuang |
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| description | We apply machine learning techniques to identify and map resurfacing units in the central South Pole−Aitken (SPA) basin using three Lunar Reconnaissance Orbiter (LRO) mission data sets: 321/415 nm and 566/689 nm band reflectance ratios from Hapke photometrically standardized albedo maps and a Terrain Ruggedness Index map using the Wilson et al. method. Other data were considered, but albedo and topography data were key in distinguishing between maria, cryptomaria, and light plains. A two-step image classification approach was applied to the data sets, an unsupervised K-Means algorithm followed by a supervised Maximum Likelihood Classification (MLC) algorithm. K-Means identified four units, one associated with dark smooth maria, two not associated with any particular features, and a fourth associated with edge effects. To further discriminate between the two nonassociated units, the K-Means unit map and an LRO morphologic basemap were used to select multiple training areas for three defined units in the MLC algorithm: mare, cryptomare, and cryptomare/light plains. From the training area values, the MLC unit map showed a distinction between the two prior indistinguishable K-Means units. Our results show (1) that the cryptomare from the MLC algorithm is in good agreement with cryptomaria mapped by J. L. Whitten & J. W. Head, (2) that the presence of scattered maria within large patches of cryptomaria indicates possible incomplete and/or uneven ejecta deposits or sheet flows covering cryptomare surfaces, and (3) a 79% increase in the total extent of cryptomaria compared to that by J. L. Whitten & J. W. Head for the same given study area in central SPA. |
| format | Article |
| id | doaj-art-659a6402acbc40a49687541212525be9 |
| institution | Kabale University |
| issn | 2632-3338 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | The Planetary Science Journal |
| spelling | doaj-art-659a6402acbc40a49687541212525be92025-02-04T10:48:33ZengIOP PublishingThe Planetary Science Journal2632-33382025-01-01623510.3847/PSJ/ada4a6Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken BasinFrank C. Chuang0https://orcid.org/0000-0001-8290-7930Matthew D. Richardson1https://orcid.org/0000-0002-9122-5082Jennifer L. Whitten2https://orcid.org/0000-0001-8068-9597Daniel P. Moriarty3https://orcid.org/0000-0001-6320-2337Deborah L. Domingue4https://orcid.org/0000-0002-7594-4634Planetary Science Institute , Tucson, AZ 85719, USAPlanetary Science Institute , Tucson, AZ 85719, USACenter for Earth and Planetary Studies/National Air and Space Museum, Smithsonian Institution , Washington, DC 20560, USANASA Goddard Space Flight Center , Greenbelt, MD 20771, USA; University of Maryland , College Park, MD 20742, USA; Center for Research and Exploration in Space Science & Technology II, NASA Goddard Space Flight Center and University of Maryland , USAPlanetary Science Institute , Tucson, AZ 85719, USAWe apply machine learning techniques to identify and map resurfacing units in the central South Pole−Aitken (SPA) basin using three Lunar Reconnaissance Orbiter (LRO) mission data sets: 321/415 nm and 566/689 nm band reflectance ratios from Hapke photometrically standardized albedo maps and a Terrain Ruggedness Index map using the Wilson et al. method. Other data were considered, but albedo and topography data were key in distinguishing between maria, cryptomaria, and light plains. A two-step image classification approach was applied to the data sets, an unsupervised K-Means algorithm followed by a supervised Maximum Likelihood Classification (MLC) algorithm. K-Means identified four units, one associated with dark smooth maria, two not associated with any particular features, and a fourth associated with edge effects. To further discriminate between the two nonassociated units, the K-Means unit map and an LRO morphologic basemap were used to select multiple training areas for three defined units in the MLC algorithm: mare, cryptomare, and cryptomare/light plains. From the training area values, the MLC unit map showed a distinction between the two prior indistinguishable K-Means units. Our results show (1) that the cryptomare from the MLC algorithm is in good agreement with cryptomaria mapped by J. L. Whitten & J. W. Head, (2) that the presence of scattered maria within large patches of cryptomaria indicates possible incomplete and/or uneven ejecta deposits or sheet flows covering cryptomare surfaces, and (3) a 79% increase in the total extent of cryptomaria compared to that by J. L. Whitten & J. W. Head for the same given study area in central SPA.https://doi.org/10.3847/PSJ/ada4a6Lunar mariaLunar featuresLunar scienceThe Moon |
| spellingShingle | Frank C. Chuang Matthew D. Richardson Jennifer L. Whitten Daniel P. Moriarty Deborah L. Domingue Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin The Planetary Science Journal Lunar maria Lunar features Lunar science The Moon |
| title | Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin |
| title_full | Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin |
| title_fullStr | Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin |
| title_full_unstemmed | Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin |
| title_short | Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin |
| title_sort | application of machine learning techniques to distinguish between mare cryptomare and light plains in central lunar south pole aitken basin |
| topic | Lunar maria Lunar features Lunar science The Moon |
| url | https://doi.org/10.3847/PSJ/ada4a6 |
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