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Article

MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning

1
Mullard Space Science Laboratory, Imaging Group, Department of Space and Climate Physics, University College London, Holmbury St Mary, Surrey RH5 6NT, UK
2
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
3
Ministry of Natural Resources Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
4
Laboratoire de Planétologie et Géodynamique, CNRS, UMR 6112, Universités de Nantes, 44300 Nantes, France
*
Author to whom correspondence should be addressed.
Academic Editor: Louis Scuderi
Remote Sens. 2021, 13(21), 4220; https://doi.org/10.3390/rs13214220
Received: 23 September 2021 / Revised: 18 October 2021 / Accepted: 19 October 2021 / Published: 21 October 2021
(This article belongs to the Special Issue Mars Remote Sensing)
The High-Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter provides remotely sensed imagery at the highest spatial resolution at 25–50 cm/pixel of the surface of Mars. However, due to the spatial resolution being so high, the total area covered by HiRISE targeted stereo acquisitions is very limited. This results in a lack of the availability of high-resolution digital terrain models (DTMs) which are better than 1 m/pixel. Such high-resolution DTMs have always been considered desirable for the international community of planetary scientists to carry out fine-scale geological analysis of the Martian surface. Recently, new deep learning-based techniques that are able to retrieve DTMs from single optical orbital imagery have been developed and applied to single HiRISE observational data. In this paper, we improve upon a previously developed single-image DTM estimation system called MADNet (1.0). We propose optimisations which we collectively call MADNet 2.0, which is based on a supervised image-to-height estimation network, multi-scale DTM reconstruction, and 3D co-alignment processes. In particular, we employ optimised single-scale inference and multi-scale reconstruction (in MADNet 2.0), instead of multi-scale inference and single-scale reconstruction (in MADNet 1.0), to produce more accurate large-scale topographic retrieval with boosted fine-scale resolution. We demonstrate the improvements of the MADNet 2.0 DTMs produced using HiRISE images, in comparison to the MADNet 1.0 DTMs and the published Planetary Data System (PDS) DTMs over the ExoMars Rosalind Franklin rover’s landing site at Oxia Planum. Qualitative and quantitative assessments suggest the proposed MADNet 2.0 system is capable of producing pixel-scale DTM retrieval at the same spatial resolution (25 cm/pixel) of the input HiRISE images. View Full-Text
Keywords: 3D mapping; digital terrain model; DTM; topography; small-scale; high-resolution; Mars; deep learning; HiRISE; HRSC; ExoMars; Oxia Planum 3D mapping; digital terrain model; DTM; topography; small-scale; high-resolution; Mars; deep learning; HiRISE; HRSC; ExoMars; Oxia Planum
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MDPI and ACS Style

Tao, Y.; Muller, J.-P.; Xiong, S.; Conway, S.J. MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning. Remote Sens. 2021, 13, 4220. https://doi.org/10.3390/rs13214220

AMA Style

Tao Y, Muller J-P, Xiong S, Conway SJ. MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning. Remote Sensing. 2021; 13(21):4220. https://doi.org/10.3390/rs13214220

Chicago/Turabian Style

Tao, Yu, Jan-Peter Muller, Siting Xiong, and Susan J. Conway. 2021. "MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning" Remote Sensing 13, no. 21: 4220. https://doi.org/10.3390/rs13214220

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