MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning Article Swipe
Yu Tao
,
Jan‐Peter Müller
,
Siting Xiong
,
Susan J. Conway
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13214220
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13214220
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs13214220
- https://www.mdpi.com/2072-4292/13/21/4220/pdf?version=1636537666
- OA Status
- gold
- Cited By
- 18
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3209096893
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3209096893Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs13214220Digital Object Identifier
- Title
-
MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-21Full publication date if available
- Authors
-
Yu Tao, Jan‐Peter Müller, Siting Xiong, Susan J. ConwayList of authors in order
- Landing page
-
https://doi.org/10.3390/rs13214220Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/13/21/4220/pdf?version=1636537666Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/13/21/4220/pdf?version=1636537666Direct OA link when available
- Concepts
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Mars Exploration Program, Remote sensing, Orbiter, Martian surface, Scale (ratio), Geology, Terrain, Pixel, Computer science, Martian, Artificial intelligence, Astrobiology, Cartography, Geography, Aerospace engineering, Physics, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 3, 2023: 5, 2022: 6Per-year citation counts (last 5 years)
- References (count)
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72Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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