Global detection of large lunar craters based on the CE-1 digital elevation model

Lei LUO, Lingli MU, Xinyuan WANG, Chao LI, Wei JI, Jinjin ZHAO, Heng CAI

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Front. Earth Sci. ›› 2013, Vol. 7 ›› Issue (4) : 456-464. DOI: 10.1007/s11707-013-0361-3
RESEARCH ARTICLE

Global detection of large lunar craters based on the CE-1 digital elevation model

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Abstract

Craters, one of the most significant features of the lunar surface, have been widely researched because they offer us the relative age of the surface unit as well as crucial geological information. Research on crater detection algorithms (CDAs) of the Moon and other planetary bodies has concentrated on detecting them from imagery data, but the computational cost of detecting large craters using images makes these CDAs impractical. This paper presents a new approach to crater detection that utilizes a digital elevation model instead of images; this enables fully automatic global detection of large craters. Craters were delineated by terrain attributes, and then thresholding maps of terrain attributes were used to transform topographic data into a binary image, finally craters were detected by using the Hough Transform from the binary image. By using the proposed algorithm, we produced a catalog of all craters≥10 km in diameter on the lunar surface and analyzed their distribution and population characteristics.

Keywords

digital elevation model / crater detection algorithm (CDA) / curvature / Hough Transform / CE-1

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Lei LUO, Lingli MU, Xinyuan WANG, Chao LI, Wei JI, Jinjin ZHAO, Heng CAI. Global detection of large lunar craters based on the CE-1 digital elevation model. Front Earth Sci, 2013, 7(4): 456‒464 https://doi.org/10.1007/s11707-013-0361-3

References

[1]
Alves E I (2003). A New Crater Recognition Method and Its Application to Images of Mars. In: Geophysical Research Abstracts. Nice: Copernicus Publications
[2]
Bandeira L, Ding W, Stepinski T F (2010). Automatic detection of sub-km craters using shape and texture in formation. In: Proceedings of the 41st Lunar and Planetary Science Conference, Abs. 1144, <month>March</month><day>1–5</day>, 2010, Houston, US
[3]
Bue B D, Stepinski T F (2007). Machine detection of Martian impact craters from digital topography data. IEEE Trans Geosci Rem Sens, 45(1): 265–274
CrossRef Google scholar
[4]
Di S F, Costantini M, DiMartino M (2002). Craters — Executive summary: survey of algorithms for automatic recognition of impact craters. ESA Contract Report, Paris, France
[5]
Earl J, Chicarro A, Koeberl C, Marchetti P G, Milnes M (2005). Automatic recognition of crater-like structures in terrestrial and planetary images. In: Proceedings of the 36th Lunar and Planetary Science Conference, Abs.1319, <month>March</month><day>14–18</day>, 2005, Houston, US
[6]
Head J W Ⅲ, Fassett C I, Kadish S J, Smith D E, Zuber M T, Neumann G A, Mazarico E (2010). Global distribution of large lunar craters: implications for resurfacing and impactor populations. Science, 329(5998): 1504–1507
CrossRef Pubmed Google scholar
[7]
Honda R, Iijima Y, Konishi O (2002). Mining of topographic feature from heterogeneous imagery and its application to lunar craters. Lecture Notes in Computer Science, 2281: 395–407
[8]
Ivanov B A, Neukum G, Bottke W F, Hartmann W K (2002). The Comparison of Size-Distributions of Impact Craters and Asteroids and the Planetary Cratering Rate, Asteroids III. Tucson: University of Arizona Press
[9]
Kadish S J, Fassett C I, Head J W, Smith D E, Zuber M T, Neumann G A, Mazarico E (2011). A global catalog of large Lunar craters (≥ 20 km) from the Lunar Orbiter Laser Altimeter. In: Proceedings of the 42nd Lunar and Planetary Science Conference, Abs. 1006, <month>March</month><day>7–11</day>, 2011, Houston, US
[10]
Kim J R, Muller J P, Stephan S V, Morley J G, Neukum G (2005). Automated crater detection, a new tool for Mars cartography and chronology. Photogrammetric Engineering & Remote Sensing, 71: 1205–1217
[11]
Li C L, Liu J J, Mu L L, Ren X (2013). The Chang’E-1 Topographic Atlas of the Moon. Beijing: SinoMaps Press (in Chinese)
[12]
Li C L, Ren X, Liu J J, Zou X D, Mu L L, Wang J Y, Shu R, Zou Y L, Zhang H B, Lü C, Liu J Z, Zuo W, Su Y, Wen W B, Bian W, Wang M, Xu C, Kong D Q, Wang X Q, Wang F, Geng L, Zhang Z B, Zheng L, Zhu X Y, Li J D, Ouyang Z Y (2010). Laser altimetry data of Chang’E-1 and the global lunar DEM model. Science China–Earth Sciences, 53(11): 1582–1593
CrossRef Google scholar
[13]
Li C, Wang X Y, Luo L, Ji W (2012). Automatic detection of lunar elliptical craters from Apollo Image. Remote Sensing for Land & Resources, 95: 71–75 (in Chinese)
[14]
Luo L, Wang X Y, Ji W, Li C (2011). Automated detection of lunar craters based on Chang’E-1 CCD data. In: Qiu P H, Xiang Y, Ding Y S, Li D M, Wang L P, eds. Proceedings of the 4th International Congress on Image and Signal Processing. Shanghai: Donghua University
[15]
Magee M, Chapman C R, Dellenback S W, Enke B, Merline W J, Rigney M P (2003). Automated identification of Martian craters using image processing. In: Proceedings of the 34th Lunar and Planetary Science Conference, Abs. 1756, <month>March</month><day>17–21</day>, 2003, Houston, US
[16]
McDowell J (2007). Lunar Nomenclature. Jonathan’s Space Report
[17]
McEwen A S, Bierhaus E B (2006). The importance of secondary cratering to age constraints on planetary surfaces. Annual Review of Earth and Planetary Sciences, 34(1): 535–567
CrossRef Google scholar
[18]
Michael G G (2003). Coordinate registration by automated crater recognition. Planetary and Space Science, 51(9–10): 563–568
CrossRef Google scholar
[19]
Neukum G, Ivanov B A (1994). Crater size distributions and impact probabilities on earth from lunar terrestrial-planet, and asteroid cratering data, Hazards Due to Comets and Asteroids. Arizona: University of Arizona
[20]
Neukum G, Ivanov B, Hartmann W K (2001). Cratering records in the inner solar system in relation to the lunar reference system. Space Science Reviews, 96: 55–86
CrossRef Google scholar
[21]
Neukum G, Konig B, Arkani J H (1975). A study of lunar impact crater size-distributions. Moon, 12: 201–229
CrossRef Google scholar
[22]
Ouyang Z Y (2005). Introduction to Lunar Science. Beijing: China Aerospace Press (in Chinese)
[23]
Ouyang Z Y, Li C L, Zou Y L, Zhang H B, Lü C, Liu J Z, Liu J J, Zuo W, Su Y, Wen W B, Bian W, Zhao B C, Wang J Y, Yang J F, Chang J, Wang H Y, Zhang X H, Wang S J, Wang M, Ren X, Mu L L, Kong D Q, Wang X Q, Wang F, Geng L, Zhang Z B, Zheng L, Zhu X Y, Zheng Y C, Li J D, Zou X D, Xu C, Shi S B, Gao Y F, Gao G N (2010). Primary scientific results of Chang’E-1 Lunar mission. Science China-Earth Sciences, 53(11): 1565–1581
CrossRef Google scholar
[24]
Rodionova J F, Karlov A A, Skobeleva T P, Konotopskaya E V, Shevchenko V V, Kozubskiy K E, Dekhtyareva K I, Smolyakova T F, Tishik L I, Fedorova E A (1987). Morphological Catalogue of the Craters of the Moon. Moscow: Mosk Gos Univ Press
[25]
Salamuniccar G, Loncaric S (2010). Method for crater detection fron Martian digital topography data using gradient value/orientation, morphometry, votes-analysis, slip-tuning and calibration. IEEE Trans Geosci Rem Sens, 48(5): 2317–2329
CrossRef Google scholar
[26]
Sawabe Y, Matsunaga T, Rokugawa S (2006). Automated detection and classification of lunar craters using multiple approaches. Advances in Space Research, 37(1): 21–27
CrossRef Google scholar
[27]
Wan C, Cheng W M, Zhou Z P, Zhao S M, Xia Y (2012). Automatic extraction of lunar impact craters from Chang’E-1 satellite photographs. Sci China Phys Mech Astron, 55(1): 162–169
CrossRef Google scholar
[28]
Yue Z Y, Liu J Z, Wu G G (2008). Automated detection of lunar craters based on object-oriented approach. Chinese Science Bulletin, 53(23): 3699–3704
CrossRef Google scholar

Acknowledgements

We express our gratitude to the National Astronomical Observatories, Chinese Academy of Sciences, which offered the data for this study. This work was supported by the National Hi-Tech Research and Development Program of China (Grant No. 2010AA122202 - 02) and the National Natural Science Foundation of China (Grant No. 60972141)

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
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