Automatic Detection and Boundary Extraction of Lunar Craters Based on LOLA DEM Data

Li, Bo; Ling, Zongcheng; Zhang, Jiang; Wu, Zhongchen

doi:10.1007/s11038-015-9467-9

Cited by 12 publications

(2 citation statements)

References 19 publications

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“…While the automatic identification of lunar impact craters has been successfully achieved (Kang, Luo, Hu, & Gamba, 2015;Vijayan, Vani, & Sanjeevi, 2013;Li, Ling, et al, 2015), to date the detection and mapping of lunar landslides has been obtained only through visual inspection of images (e.g. Brunetti et al, 2015;.…”

Section: Visual Detection Of Landslides Within Simple Impact Cratersmentioning

confidence: 99%

Recognition of landslides in lunar impact craters

Scaioni

Yordanov

Brunetti

et al. 2017

European Journal of Remote Sensing

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Landslides have been observed on several planets and minor bodies of the solar System, including the Moon. Notwithstanding different types of slope failures have been studied on the Moon, a detailed lunar landslide inventory is still pending. Undoubtedly, such will be in a benefit for future geological and morphological studies, as well in hazard, risk and suscept- ibility assessments. A preliminary survey of lunar landslides in impact craters has been done using visual inspection on images and digital elevation model (DEM) (Brunetti et al. 2015) but this method suffers from subjective interpretation. A new methodology based on polynomial interpolation of crater cross-sections extracted from global lunar DEMs is presented in this paper. Because of their properties, Chebyshev polynomials were already exploited for para- metric classification of different crater morphologies (Mahanti et al., 2014). Here, their use has been extended to the discrimination of slumps in simple impact craters. Two criteria for recognition have provided the best results: one based on fixing an empirical absolute thresholding and a second based on statistical adaptive thresholding. The application of both criteria to a data set made up of 204 lunar craters’ cross-sections has demonstrated that the former criterion provides the best recognition

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Section: Visual Detection Of Landslides Within Simple Impact Cratersmentioning

confidence: 99%

Recognition of landslides in lunar impact craters

Scaioni

Yordanov

Brunetti

et al. 2017

European Journal of Remote Sensing

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show abstract

“…Results from automated crater detection algorithms using only DEMs and not imagery have been reported in the past for crater finding on both the Moon (Luo et al 2013;Xie et al 2013;Li et al 2015) and Mars (Bue & Stepinski 2007;Stepinski et al 2009;Salamuniccar & Loncaric 2010;Di et al 2014), but not yet Mercury. As the focus of this paper is on deep, symmetric craters, a relatively simple crater detection algorithm has been designed that should recover such features from the MLA DEM.…”

Section: Crater Findingmentioning

confidence: 87%

How thick are Mercury’s polar water ice deposits?

Eke

Lawrence

Teodoro

2017

Icarus

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An estimate is made of the thickness of the radar-bright deposits in craters near to Mercury's north pole. To construct an objective set of craters for this measurement, an automated crater finding algorithm is developed and applied to a digital elevation model based on data from the Mercury Laser Altimeter onboard the MESSENGER spacecraft. This produces a catalogue of 663 craters with diameters exceeding 4 km, northwards of latitude +55• . A subset of 12 larger, well-sampled and fresh polar craters are selected to search for correlations between topography and radar same-sense backscatter cross-section. It is found that the typical excess height associated with the radar-bright regions within these fresh polar craters is (50 ± 35) m. This puts an approximate upper limit on the total polar water ice deposits on Mercury of ∼ 3 × 10 15 kg.

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A New Approach Based on Crater Detection and Matching for Self-Localization During Lunar Landings

Qian

Cheng

et al. 2022

Lecture Notes in Electrical Engineering

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Automatic Detection and Boundary Extraction of Lunar Craters Based on LOLA DEM Data

Cited by 12 publications

References 19 publications

Recognition of landslides in lunar impact craters

Recognition of landslides in lunar impact craters

How thick are Mercury’s polar water ice deposits?

A New Approach Based on Crater Detection and Matching for Self-Localization During Lunar Landings

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