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Automatically calculating the apparent depths of pits using the Pit Topography from Shadows (PITS) tool

Le Corre, Daniel, Mary, David, Mason, Nigel, Bernard-Salas, Jeronimo, Cox, Nick (2023) Automatically calculating the apparent depths of pits using the Pit Topography from Shadows (PITS) tool. RAS Techniques and Instruments, 2 (1). pp. 492-509. ISSN 2752-8200. (doi:10.1093/rasti/rzad037) (KAR id:102520)

Abstract

Pits, or pit craters, are near-circular depressions found in planetary surfaces, which are generally formed through gravitational collapse. Pits will be primary targets for future space exploration and habitability for their presence on most rocky Solar System surfaces and their potential to be entrances to sub-surface cavities. This is particularly true on Mars, where caves have been simulated to harbour stable reserves of ice water across much of the surface. Caves can also provide natural shelter from the high radiation dosages experienced at the surface. Since pits are rarely found to have corresponding high-resolution elevation data, tools are required for approximating their depths in order to find those which are the ideal candidates for follow-up remote investigation and future exploration. The Pit Topography from Shadows (PITS) tool has been developed to automatically calculate the apparent depth of a pit (h) by measuring the width of its shadow as it appears in satellite imagery. The tool requires just one cropped single- or multi-band image of a pit to calculate a profile of h along the length of the shadow, thus allowing for depth calculation where altimetry or stereo image data is not available. We also present a method for correcting shadow width measurements made in non-nadir observations for all possible values of emission and solar/satellite azimuth angles. Shadows are extracted using image segmentation in the form of k-means clustering and silhouette analysis. Across 19 shadow-labelled Mars Reconnaissance Orbiter red-band HiRISE images of Atypical Pit Craters (APCs) from the Mars Global Cave Candidate Catalog (MGC3), PITS detected 99.6 per cent of all shadow pixels (with 94.8 per cent of all detections being true shadow pixels). Following this testing, PITS has been applied to 123 red-band HiRISE images containing 88 APCs, which revealed an improvement in the variation of the calculated h due to emission angle correction, and also found 10 APCs that could be good candidates for cave entrances on Mars due to their h profiles.

Item Type: Article
DOI/Identification number: 10.1093/rasti/rzad037
Uncontrolled keywords: Machine Learning – Algorithms – Mars – Planetary surfaces – Pits – Cave entrances
Subjects: Q Science
Divisions: Divisions > Division of Natural Sciences > Physics and Astronomy
Funders: European Union (https://ror.org/019w4f821)
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 23 Aug 2023 13:05 UTC
Last Modified: 08 Apr 2024 09:56 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/102520 (The current URI for this page, for reference purposes)

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