Machine cataloging of impact craters on Mars

Stepinski, T. F.; Mendenhall, M. P.; Bue, Brian D.

doi:10.1016/j.icarus.2009.04.026

Cited by 72 publications

(48 citation statements)

References 34 publications

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“…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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Section: Crater Findingmentioning

confidence: 87%

How thick are Mercury’s polar water ice deposits?

Eke

Lawrence

Teodoro

2017

Icarus

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“…The crater depth authenticity is purely depends on the DTM image quality, which intern is highly depend on the ortho image computed form the stereo pair images. Stepinski et al (2009) notified concern that the depth of the small craters should not be used individually, rather it is for statistical context. Similarly, our results on estimated crater depth insist the same for the statistical context.…”

Section: Evaluation Of Cda Detection and Classification Performancementioning

confidence: 99%

“…Image-based approach widely uses techniques such as gradient approach and pattern recognition to extract craters (Sawabe et al, 2006;Urbach and Stepinski, 2009). DEM/DTM-based approach uses machine learning, identify craters by depression, etc., (Kim et al, 2005;Stepinski et al, 2009). However, almost all the previous CDA are restricted to detection and counting (Bue and Stepinski, 2007;Salamuniccar et al, 2011) and further information about each crater was lacking.…”

Section: Introductionmentioning

confidence: 99%

Crater detection, classification and contextual information extraction in lunar images using a novel algorithm

Vijayan

Vani

Sanjeevi

2013

Icarus

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“…Some authors (Salamaniccar & Loncaric, 2010) ) (Stepinski, Mendenhall, & Bue, Robust automated identification of martian impact craters, 2007) identify craters in a DEM in a manner similar to the identification of craters in an imagethrough rim detection. Alternatively, (Stepinski, Mendenhall, & Bue., 2009) fully utilize tree-dimensional character of the DEM data and identify craters from DEM as round-shaped depression of certain depth. Overall, it is preferable to detect craters from DEMs than from images.…”

Section: Approaches To Auto--detection Of Cratersmentioning

confidence: 99%

Detecting Impact Craters in Planetary Images Using Machine Learning

Stepinski

Ding

Vilalta

2012

Intelligent Data Analysis for Real-Life Applications

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Prompted by crater counts as the only available tool for measuring remotely the relative ages of geologic formations on planets, advances in remote sensing have produced a very large database of high resolution planetary images, opening up an opportunity to survey much more numerous small craters improving the spatial and temporal resolution of stratigraphy. Automating the process of crater detection is key to generate comprehensive surveys of smaller craters. Here we discuss two supervised machine learning techniques for crater detection algorithms (CDA): identification of craters from digital elevation models (also known as range images), and identification of craters from panchromatic images. We present applications of both techniques and demonstrate how such automated analysis has produced new knowledge about planet Mars.

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Machine cataloging of impact craters on Mars

Cited by 72 publications

References 34 publications

How thick are Mercury’s polar water ice deposits?

How thick are Mercury’s polar water ice deposits?

Crater detection, classification and contextual information extraction in lunar images using a novel algorithm

Detecting Impact Craters in Planetary Images Using Machine Learning

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