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Planetary 3D Mapping, Remote Sensing and Machine Learning
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Planetary 3D Mapping, Remote Sensing and Machine Learning

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 55014

Special Issue Editors

Prof. Dr. Jan-Peter Muller

E-Mail Website
Guest Editor
Emeritus Professor, Mullard Space Science Laboratory, Department of Space & Climate Physics, University College London (UCL), Holmbury St Mary RH5 6NT, UK
Interests: deep learning for change detection on Mars; 3D imaging for Mars and the Moon; orbital-rover image fusion; subsurface mapping; super-resolution restoration; surface albedo; cloud heights and winds; globe imaging; VR
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Wu

E-Mail Website
Guest Editor
Department of Land Surveying & Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: planetary remote sensing and mapping; 3D mapping using photogrammetry and shape-from-shading; crater and rock detection on planetary datasets
Mr. Trent Hare

E-Mail Website
Guest Editor
Astrogeology Science Center, 2255 North Gemini Drive, Flagstaff, AZ 86001, USA
Interests: planetary remote sensing; photogrammetry; GIS and 3d mapping; data set interoperability; creation of geospatial tools; cartographic representations; metadata and data archiving; web-based mapping; cloud computing and data streaming technologies.

Special Issue Information

Dear Colleagues,

Our knowledge and understanding of the physical processes of the Earth and Planets within our Solar System have been enormously enhanced since space-based remote sensing and photogrammetry was applied from orbital platforms in the 1960s.

Over the last two decades, the pace of technology development and the associated quality of space-based data has accelerated to a point where we have details on planetary surfaces comparable to what we have on the Earth.

We would like to invite you to submit articles on new methods and their applications to 3D mapping of surfaces (both solid and subsurface as well as cloud or aerosol), to landscape characterisation to new methods using deep learning and machine vision to different wavelengths including hyperspectral, visible to thermal IR, microwave and laser-based methods. Although the emphasis will be on orbital data, papers are also sought on robotic imaging systems and their fusion with space-based data.

We therefore seek original research articles covering all aspects of planetary remote sensing and 3D mapping including new instruments, methods, algorithms, datasets and validation.

We look forward to receiving your submissions which will be vigorously triaged and reviewed within a much shorter turnaround time than most current journals.

Prof. Jan-Peter Muller
Dr. Bo Wu
Mr. Trent Hare
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Planetary 3D mapping, clouds, aerosols, solid surfaces and subsurfaces
  • Planetary remote sensing techniques and new instrument technologies
  • Planetary topography: photogrammetry, shape-from-shading, and laser altimetry
  • Planetary geomorphology: craters, domes, rocks/boulders, ridges/rills, dunes, etc.
  • Machine learning applied to planetary mapping and remote sensing
  • Robotic image simulations and fusion with orbital data
  • Machine learning applied to planetary landscape characterisation in rover and orbital images and change detection in remote sensing
  • Data product dissemination, formats, interoperability
  • Web GIS applied to planetary remote sensing data
  • Cloud computing and data streaming/analysis methods for large planetary data sets

Published Papers (16 papers)

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25 pages, 10557 KiB  
Article
Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM Estimation and Super-Resolution Restoration
by Yu Tao, Siting Xiong, Jan-Peter Muller, Greg Michael, Susan J. Conway, Gerhard Paar, Gabriele Cremonese and Nicolas Thomas
Remote Sens. 2022, 14(2), 257; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020257 - 06 Jan 2022
Cited by 4 | Viewed by 2499
Abstract
We propose using coupled deep learning based super-resolution restoration (SRR) and single-image digital terrain model (DTM) estimation (SDE) methods to produce subpixel-scale topography from single-view ESA Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) and NASA Mars Reconnaissance Orbiter High Resolution [...] Read more.
We propose using coupled deep learning based super-resolution restoration (SRR) and single-image digital terrain model (DTM) estimation (SDE) methods to produce subpixel-scale topography from single-view ESA Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) and NASA Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment (HiRISE) images. We present qualitative and quantitative assessments of the resultant 2 m/pixel CaSSIS SRR DTM mosaic over the ESA and Roscosmos Rosalind Franklin ExoMars rover’s (RFEXM22) planned landing site at Oxia Planum. Quantitative evaluation shows SRR improves the effective resolution of the resultant CaSSIS DTM by a factor of 4 or more, while achieving a fairly good height accuracy measured by root mean squared error (1.876 m) and structural similarity (0.607), compared to the ultra-high-resolution HiRISE SRR DTMs at 12.5 cm/pixel. We make available, along with this paper, the resultant CaSSIS SRR image and SRR DTM mosaics, as well as HiRISE full-strip SRR images and SRR DTMs, to support landing site characterisation and future rover engineering for the RFEXM22. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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49 pages, 19325 KiB  
Article
How Well Do We Know Europa’s Topography? An Evaluation of the Variability in Digital Terrain Models of Europa
by Michael T. Bland, Randolph L. Kirk, Donna M. Galuszka, David P. Mayer, Ross A. Beyer and Robin L. Fergason
Remote Sens. 2021, 13(24), 5097; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245097 - 15 Dec 2021
Cited by 6 | Viewed by 2674
Abstract
Jupiter’s moon Europa harbors one of the most likely environments for extant extraterrestrial life. Determining whether Europa is truly habitable requires understanding the structure and thickness of its ice shell, including the existence of perched water or brines. Stereo-derived topography from images acquired [...] Read more.
Jupiter’s moon Europa harbors one of the most likely environments for extant extraterrestrial life. Determining whether Europa is truly habitable requires understanding the structure and thickness of its ice shell, including the existence of perched water or brines. Stereo-derived topography from images acquired by NASA Galileo’s Solid State Imager (SSI) of Europa are often used as a constraint on ice shell structure and heat flow, but the uncertainty in such topography has, to date, not been rigorously assessed. To evaluate the current uncertainty in Europa’s topography we generated and compared digital terrain models (DTMs) of Europa from SSI images using both the open-source Ames Stereo Pipeline (ASP) software and the commercial SOCET SET® software. After first describing the criteria for assessing stereo quality in detail, we qualitatively and quantitatively describe both the horizontal resolution and vertical precision of the DTMs. We find that the horizontal resolution of the SOCET SET® DTMs is typically 8–11× the root mean square (RMS) pixel scale of the images, whereas the resolution of the ASP DTMs is 9–13× the maximum pixel scale of the images. We calculate the RMS difference between the ASP and SOCET SET® DTMs as a proxy for the expected vertical precision (EP), which is a function of the matching accuracy and stereo geometry. We consistently find that the matching accuracy is ~0.5 pixels, which is larger than well-established “rules of thumb” that state that the matching accuracy is 0.2–0.3 pixels. The true EP is therefore ~1.7× larger than might otherwise be assumed. In most cases, DTM errors are approximately normally distributed, and errors that are several times the derived EP occur as expected. However, in two DTMs, larger errors (differences) occur and correlate with real topography. These differences primarily result from manual editing of the SOCET SET® DTMs. The product of the DTM error and the resolution is typically 4–8 pixel2 if calculated using the RMS image scale for SOCET SET® DTMs and the maximum images scale for the ASP DTMs, which is consistent with recent work using martian data sets and suggests that the relationship applies more broadly. We evaluate how ASP parameters affect DTM quality and find that using a smaller subpixel refinement kernel results in DTMs with smaller (better) resolution but, in some cases, larger gaps, which are sometimes reduced by increasing the size of the correlation kernel. We conclude that users of ASP should always systematically evaluate the choice of parameters for a given dataset. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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40 pages, 26105 KiB  
Article
Evaluating Stereo Digital Terrain Model Quality at Mars Rover Landing Sites with HRSC, CTX, and HiRISE Images
by Randolph L. Kirk, David P. Mayer, Robin L. Fergason, Bonnie L. Redding, Donna M. Galuszka, Trent M. Hare and Klaus Gwinner
Remote Sens. 2021, 13(17), 3511; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173511 - 04 Sep 2021
Cited by 15 | Viewed by 3170
Abstract
We have used high-resolution digital terrain models (DTMs) of two rover landing sites based on mosaicked images from the High-Resolution Imaging Science Experiment (HiRISE) camera as a reference to evaluate DTMs based on High-Resolution Stereo Camera (HRSC) and Context Camera (CTX) images. The [...] Read more.
We have used high-resolution digital terrain models (DTMs) of two rover landing sites based on mosaicked images from the High-Resolution Imaging Science Experiment (HiRISE) camera as a reference to evaluate DTMs based on High-Resolution Stereo Camera (HRSC) and Context Camera (CTX) images. The Next-Generation Automatic Terrain Extraction (NGATE) matcher in the SOCET SET and GXP® commercial photogrammetric systems produces DTMs with good (small) horizontal resolution but large vertical error. Somewhat surprisingly, results for NGATE are terrain dependent, with poorer resolution and smaller errors on smoother surfaces. Multiple approaches to smoothing the NGATE DTMs give similar tradeoffs between resolution and error; a 5 × 5 lowpass filter is near optimal in terms of both combined resolution-error performance and local slope estimation. Smoothing with an area-based matcher, the standard processing for U.S. Geological Survey planetary DTMs, yields similar errors to the 5 × 5 filter at slightly worse resolution. DTMs from the HRSC team processing pipeline fall within this same trade space but are less sensitive to terrain roughness. DTMs produced with the Ames Stereo Pipeline also fall in this space at resolutions intermediate between NGATE and the team pipeline. Considered individually, resolution and error each varied by approximately a factor of 2. Matching errors were 0.2–0.5 pixels but most results fell in the 0.2–0.3 pixel range that has been stated as a rule of thumb in multiple prior studies. Horizontal resolutions of 10–20 image pixels were found, consistently greater than the 3–5 pixel spacing generally used for stereo DTM production. Resolution and precision were inversely correlated; their product varied by ≤20% (4–5 pixels squared). Refinement of the stereo DTM by photoclinometry can yield quantitative improvement in resolution (more than a factor of 2), provided that albedo variations over distances smaller than the stereo DTM resolution are not too severe. We offer specific guidance for both producers and users of planetary stereo DTMs, based on our results. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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27 pages, 13720 KiB  
Article
Large Area High-Resolution 3D Mapping of Oxia Planum: The Landing Site for the ExoMars Rosalind Franklin Rover
by Yu Tao, Jan-Peter Muller, Susan J. Conway and Siting Xiong
Remote Sens. 2021, 13(16), 3270; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163270 - 18 Aug 2021
Cited by 9 | Viewed by 2397
Abstract
We demonstrate an end-to-end application of the in-house deep learning-based surface modelling system, called MADNet, to produce three large area 3D mapping products from single images taken from the ESA Mars Express’s High Resolution Stereo Camera (HRSC), the NASA Mars Reconnaissance Orbiter’s Context [...] Read more.
We demonstrate an end-to-end application of the in-house deep learning-based surface modelling system, called MADNet, to produce three large area 3D mapping products from single images taken from the ESA Mars Express’s High Resolution Stereo Camera (HRSC), the NASA Mars Reconnaissance Orbiter’s Context Camera (CTX), and the High Resolution Imaging Science Experiment (HiRISE) imaging data over the ExoMars 2022 Rosalind Franklin rover’s landing site at Oxia Planum on Mars. MADNet takes a single orbital optical image as input, provides pixelwise height predictions, and uses a separate coarse Digital Terrain Model (DTM) as reference, to produce a DTM product from the given input image. Initially, we demonstrate the resultant 25 m/pixel HRSC DTM mosaic covering an area of 197 km × 182 km, providing fine-scale details to the 50 m/pixel HRSC MC-11 level-5 DTM mosaic. Secondly, we demonstrate the resultant 12 m/pixel CTX MADNet DTM mosaic covering a 114 km × 117 km area, showing much more detail in comparison to photogrammetric DTMs produced using the open source in-house developed CASP-GO system. Finally, we demonstrate the resultant 50 cm/pixel HiRISE MADNet DTM mosaic, produced for the first time, covering a 74.3 km × 86.3 km area of the 3-sigma landing ellipse and partially the ExoMars team’s geological characterisation area. The resultant MADNet HiRISE DTM mosaic shows fine-scale details superior to existing Planetary Data System (PDS) HiRISE DTMs and covers a larger area that is considered difficult for existing photogrammetry and photoclinometry pipelines to achieve, especially given the current limitations of stereo HiRISE coverage. All of the resultant DTM mosaics are co-aligned with each other, and ultimately with the Mars Global Surveyor’s Mars Orbiter Laser Altimeter (MOLA) DTM, providing high spatial and vertical congruence. In this paper, technical details are presented, issues that arose are discussed, along with a visual evaluation and quantitative assessments of the resultant DTM mosaic products. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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30 pages, 9471 KiB  
Article
A Photogrammetric-Photometric Stereo Method for High-Resolution Lunar Topographic Mapping Using Yutu-2 Rover Images
by Man Peng, Kaichang Di, Yexin Wang, Wenhui Wan, Zhaoqin Liu, Jia Wang and Lichun Li
Remote Sens. 2021, 13(15), 2975; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152975 - 28 Jul 2021
Cited by 7 | Viewed by 2952
Abstract
Topographic products are important for mission operations and scientific research in lunar exploration. In a lunar rover mission, high-resolution digital elevation models are typically generated at waypoints by photogrammetry methods based on rover stereo images acquired by stereo cameras. In case stereo images [...] Read more.
Topographic products are important for mission operations and scientific research in lunar exploration. In a lunar rover mission, high-resolution digital elevation models are typically generated at waypoints by photogrammetry methods based on rover stereo images acquired by stereo cameras. In case stereo images are not available, the stereo-photogrammetric method will not be applicable. Alternatively, photometric stereo method can recover topographic information with pixel-level resolution from three or more images, which are acquired by one camera under the same viewing geometry with different illumination conditions. In this research, we extend the concept of photometric stereo to photogrammetric-photometric stereo by incorporating collinearity equations into imaging irradiance model. The proposed photogrammetric-photometric stereo algorithm for surface construction involves three steps. First, the terrain normal vector in object space is derived from collinearity equations, and image irradiance equation for close-range topographic mapping is determined. Second, based on image irradiance equations of multiple images, the height gradients in image space can be solved. Finally, the height map is reconstructed through global least-squares surface reconstruction with spectral regularization. Experiments were carried out using simulated lunar rover images and actual lunar rover images acquired by Yutu-2 rover of Chang’e-4 mission. The results indicate that the proposed method achieves high-resolution and high-precision surface reconstruction, and outperforms the traditional photometric stereo methods. The proposed method is valuable for ground-based lunar surface reconstruction and can be applicable to surface reconstruction of Earth and other planets. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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40 pages, 19445 KiB  
Article
Single Image Super-Resolution Restoration of TGO CaSSIS Colour Images: Demonstration with Perseverance Rover Landing Site and Mars Science Targets
by Yu Tao, Susan J. Conway, Jan-Peter Muller, Alfiah R. D. Putri, Nicolas Thomas and Gabriele Cremonese
Remote Sens. 2021, 13(9), 1777; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091777 - 02 May 2021
Cited by 18 | Viewed by 3851
Abstract
The ExoMars Trace Gas Orbiter (TGO)’s Colour and Stereo Surface Imaging System (CaSSIS) provides multi-spectral optical imagery at 4–5 m/pixel spatial resolution. Improving the spatial resolution of CaSSIS images would allow greater amounts of scientific information to be extracted. In this work, we [...] Read more.
The ExoMars Trace Gas Orbiter (TGO)’s Colour and Stereo Surface Imaging System (CaSSIS) provides multi-spectral optical imagery at 4–5 m/pixel spatial resolution. Improving the spatial resolution of CaSSIS images would allow greater amounts of scientific information to be extracted. In this work, we propose a novel Multi-scale Adaptive weighted Residual Super-resolution Generative Adversarial Network (MARSGAN) for single-image super-resolution restoration of TGO CaSSIS images, and demonstrate how this provides an effective resolution enhancement factor of about 3 times. We demonstrate with qualitative and quantitative assessments of CaSSIS SRR results over the Mars2020 Perseverance rover’s landing site. We also show examples of similar SRR performance over 8 science test sites mainly selected for being covered by HiRISE at higher resolution for comparison, which include many features unique to the Martian surface. Application of MARSGAN will allow high resolution colour imagery from CaSSIS to be obtained over extensive areas of Mars beyond what has been possible to obtain to date from HiRISE. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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14 pages, 6467 KiB  
Article
A Study about the Temporal Constraints on the Martian Yardangs’ Development in Medusae Fossae Formation
by Jia Liu, Zongyu Yue, Kaichang Di, Sheng Gou and Shengli Niu
Remote Sens. 2021, 13(7), 1316; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071316 - 30 Mar 2021
Cited by 3 | Viewed by 1759
Abstract
The age of Mars yardangs is significant in studying their development and the evolution of paleoclimate conditions. For planetary surface or landforms, a common method for dating is based on the frequency and size distribution of all the superposed craters after they are [...] Read more.
The age of Mars yardangs is significant in studying their development and the evolution of paleoclimate conditions. For planetary surface or landforms, a common method for dating is based on the frequency and size distribution of all the superposed craters after they are formed. However, there is usually a long duration for the yardangs’ formation, and they will alter the superposed craters, making it impossible to give a reliable dating result with the method. An indirect method by analyzing the ages of the superposed layered ejecta was devised in the research. First, the layered ejecta that are superposed on and not altered by the yardangs are identified and mapped. Then, the ages of the layered ejecta are derived according to the crater frequency and size distribution on them. These ages indicate that the yardangs ceased development by these times, and the ages are valuable for studying the evolution of the yardangs. This indirect dating method was applied to the areas of Martian yardangs in the Medusae Fossae Formation (MFF). The ages of the selected six layered ejecta range from ~0.50 Ga to ~1.5 Ga, indicating that the evolution of the corresponding yardangs had been ceased before these times. Analysis of more layered ejecta craters and superposed yardangs implies that yardangs in the MFF have a long history of development and some yardangs are still in active development. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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25 pages, 26694 KiB  
Article
Particle Size-Frequency Distributions of the OSIRIS-REx Candidate Sample Sites on Asteroid (101955) Bennu
by Keara N. Burke, Daniella N. DellaGiustina, Carina A. Bennett, Kevin J. Walsh, Maurizio Pajola, Edward B. Bierhaus, Michael C. Nolan, William V. Boynton, Juliette I. Brodbeck, Harold C. Connolly, Jr., Jasinghege Don Prasanna Deshapriya, Jason P. Dworkin, Catherine M. Elder, Dathon R. Golish, Rachael H. Hoover, Erica R. Jawin, Timothy J. McCoy, Patrick Michel, Jamie L. Molaro, Jennifer O. Nolau, Jacob Padilla, Bashar Rizk, Stuart J. Robbins, Eric M. Sahr, Peter H. Smith, Stephanie J. Stewart, Hannah C. M. Susorney, Heather L. Enos and Dante S. Laurettaadd Show full author list remove Hide full author list
Remote Sens. 2021, 13(7), 1315; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071315 - 30 Mar 2021
Cited by 32 | Viewed by 5203
Abstract
We manually mapped particles ranging in longest axis from 0.3 cm to 95 m on (101955) Bennu for the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) asteroid sample return mission. This enabled the mission to identify candidate sample collection sites and [...] Read more.
We manually mapped particles ranging in longest axis from 0.3 cm to 95 m on (101955) Bennu for the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) asteroid sample return mission. This enabled the mission to identify candidate sample collection sites and shed light on the processes that have shaped the surface of this rubble-pile asteroid. Building on a global survey of particles, we used higher-resolution data from regional observations to calculate particle size-frequency distributions (PSFDs) and assess the viability of four candidate sites for sample collection (presence of unobstructed particles ≤ 2 cm). The four candidate sites have common characteristics: each is situated within a crater with a relative abundance of sampleable material. Their PSFDs, however, indicate that each site has experienced different geologic processing. The PSFD power-law slopes range from −3.0 ± 0.2 to −2.3 ± 0.1 across the four sites, based on images with a 0.01-m pixel scale. These values are consistent with, or shallower than, the global survey measurements. At one site, Osprey, the particle packing density appears to reach geometric saturation. We evaluate the uncertainty in these measurements and discuss their implications for other remotely sensed and mapped particles, and their importance to OSIRIS-REx sampling operations. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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24 pages, 7434 KiB  
Article
Unsupervised Machine Learning on Domes in the Lunar Gardner Region: Implications for Dome Classification and Local Magmatic Activities on the Moon
by Yuchao Chen, Qian Huang, Jiannan Zhao and Xiangyun Hu
Remote Sens. 2021, 13(5), 845; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050845 - 24 Feb 2021
Cited by 7 | Viewed by 2117
Abstract
Lunar volcanic domes are essential windows into the local magmatic activities on the Moon. Classification of domes is a useful way to figure out the relationship between dome appearances and formation processes. Previous studies of dome classification were manually or semi-automatically carried out [...] Read more.
Lunar volcanic domes are essential windows into the local magmatic activities on the Moon. Classification of domes is a useful way to figure out the relationship between dome appearances and formation processes. Previous studies of dome classification were manually or semi-automatically carried out either qualitatively or quantitively. We applied an unsupervised machine-learning method to domes that are annularly or radially distributed around Gardner, a unique central-vent volcano located in the northern part of the Mare Tranquillitatis. High-resolution lunar imaging and spectral data were used to extract morphometric and spectral properties of domes in both the Gardner volcano and its surrounding region in the Mare Tranquillitatis. An integrated robust Fuzzy C-Means clustering algorithm was performed on 120 combinations of five morphometric (diameter, area, height, surface volume, and slope) and two elemental features (FeO and TiO2 contents) to find the optimum combination. Rheological features of domes and their dike formation parameters were calculated for dome-forming lava explanations. Results show that diameter, area, surface volume, and slope are the selected optimum features for dome clustering. 54 studied domes can be grouped into four dome clusters (DC1 to DC4). DC1 domes are relatively small, steep, and close to the Gardner volcano, with forming lavas of high viscosities and low effusion rates, representing the latest Eratosthenian dome formation stage of the Gardner volcano. Domes of DC2 to DC4 are relatively large, smooth, and widely distributed, with forming lavas of low viscosities and high effusion rates, representing magmatic activities varying from Imbrian to Eratosthenian in the northern Mare Tranquillitatis. The integrated algorithm provides a new and independent way to figure out the representative properties of lunar domes and helps us further clarify the relationship between dome clusters and local magma activities of the Moon. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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21 pages, 7394 KiB  
Article
Mars3DNet: CNN-Based High-Resolution 3D Reconstruction of the Martian Surface from Single Images
by Zeyu Chen, Bo Wu and Wai Chung Liu
Remote Sens. 2021, 13(5), 839; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050839 - 24 Feb 2021
Cited by 16 | Viewed by 3757
Abstract
Three-dimensional (3D) surface models, e.g., digital elevation models (DEMs), are important for planetary exploration missions and scientific research. Current DEMs of the Martian surface are mainly generated by laser altimetry or photogrammetry, which have respective limitations. Laser altimetry cannot produce high-resolution DEMs; photogrammetry [...] Read more.
Three-dimensional (3D) surface models, e.g., digital elevation models (DEMs), are important for planetary exploration missions and scientific research. Current DEMs of the Martian surface are mainly generated by laser altimetry or photogrammetry, which have respective limitations. Laser altimetry cannot produce high-resolution DEMs; photogrammetry requires stereo images, but high-resolution stereo images of Mars are rare. An alternative is the convolutional neural network (CNN) technique, which implicitly learns features by assigning corresponding inputs and outputs. In recent years, CNNs have exhibited promising performance in the 3D reconstruction of close-range scenes. In this paper, we present a CNN-based algorithm that is capable of generating DEMs from single images; the DEMs have the same resolutions as the input images. An existing low-resolution DEM is used to provide global information. Synthetic and real data, including context camera (CTX) images and DEMs from stereo High-Resolution Imaging Science Experiment (HiRISE) images, are used as training data. The performance of the proposed method is evaluated using single CTX images of representative landforms on Mars, and the generated DEMs are compared with those obtained from stereo HiRISE images. The experimental results show promising performance of the proposed method. The topographic details are well reconstructed, and the geometric accuracies achieve root-mean-square error (RMSE) values ranging from 2.1 m to 12.2 m (approximately 0.5 to 2 pixels in the image space). The experimental results show that the proposed CNN-based method has great potential for 3D surface reconstruction in planetary applications. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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37 pages, 68237 KiB  
Article
High Resolution Digital Terrain Models of Mercury
by Moritz Tenthoff, Kay Wohlfarth and Christian Wöhler
Remote Sens. 2020, 12(23), 3989; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233989 - 06 Dec 2020
Cited by 11 | Viewed by 4441
Abstract
We refined our Shape from Shading (SfS) algorithm, which has previously been used to create digital terrain models (DTMs) of the Lunar and Martian surfaces, to generate high-resolution DTMs of Mercury from MESSENGER imagery. To adapt the reconstruction procedure to the specific conditions [...] Read more.
We refined our Shape from Shading (SfS) algorithm, which has previously been used to create digital terrain models (DTMs) of the Lunar and Martian surfaces, to generate high-resolution DTMs of Mercury from MESSENGER imagery. To adapt the reconstruction procedure to the specific conditions of Mercury and the available imagery, we introduced two methodic innovations. First, we extended the SfS algorithm to enable the 3D-reconstruction from image mosaics. Because most mosaic tiles were acquired at different times and under various illumination conditions, the brightness of adjacent tiles may vary. Brightness variations that are not fully captured by the reflectance model may yield discontinuities at tile borders. We found that the relaxation of the constraint for a continuous albedo map improves the topographic results of an extensive region removing discontinuities at tile borders. The second innovation enables the generation of accurate DTMs from images with substantial albedo variations, such as hollows. We employed an iterative procedure that initializes the SfS algorithm with the albedo map that was obtained by the previous iteration step. This approach converges and yields a reasonable albedo map and topography. With these approaches, we generated DTMs of several science targets such as the Rachmaninoff basin, Praxiteles crater, fault lines, and several hollows. To evaluate the results, we compared our DTMs with stereo DTMs and laser altimeter data. In contrast to coarse laser altimetry tracks and stereo algorithms, which tend to be affected by interpolation artifacts, SfS can generate DTMs almost at image resolution. The root mean squared errors (RMSE) at our target sites are below the size of the horizontal image resolution. For some targets, we could achieve an effective resolution of less than 10 m/pixel, which is the best resolution of Mercury to date. We critically discuss the limitations of the evaluation methodology. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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38 pages, 48684 KiB  
Article
DoMars16k: A Diverse Dataset for Weakly Supervised Geomorphologic Analysis on Mars
by Thorsten Wilhelm, Melina Geis, Jens Püttschneider, Timo Sievernich, Tobias Weber, Kay Wohlfarth and Christian Wöhler
Remote Sens. 2020, 12(23), 3981; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233981 - 04 Dec 2020
Cited by 22 | Viewed by 6117
Abstract
Mapping planetary surfaces is an intricate task that forms the basis for many geologic, geomorphologic, and geographic studies of planetary bodies. In this work, we present a method to automate a specific type of planetary mapping, geomorphic mapping, taking machine learning as a [...] Read more.
Mapping planetary surfaces is an intricate task that forms the basis for many geologic, geomorphologic, and geographic studies of planetary bodies. In this work, we present a method to automate a specific type of planetary mapping, geomorphic mapping, taking machine learning as a basis. Additionally, we introduce a novel dataset, termed DoMars16k, which contains 16,150 samples of fifteen different landforms commonly found on the Martian surface. We use a convolutional neural network to establish a relation between Mars Reconnaissance Orbiter Context Camera images and the landforms of the dataset. Afterwards, we employ a sliding-window approach in conjunction with a Markov Random field smoothing to create maps in a weakly supervised fashion. Finally, we provide encouraging results and carry out automated geomorphological analyses of Jezero crater, the Mars2020 landing site, and Oxia Planum, the prospective ExoMars landing site. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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19 pages, 24630 KiB  
Article
A Global Gravity Reconstruction Method for Mercury Employing Deep Convolutional Neural Network
by Shuheng Zhao, Denghong Liu, Qiangqiang Yuan and Jie Li
Remote Sens. 2020, 12(14), 2293; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142293 - 17 Jul 2020
Cited by 3 | Viewed by 2250
Abstract
Mercury, the enigmatic innermost planet in the solar system, is one of the most important targets of space exploration. High-quality gravity field data are significant to refine the physical characterization of Mercury in planetary exploration missions. However, Mercury’s gravity model is limited by [...] Read more.
Mercury, the enigmatic innermost planet in the solar system, is one of the most important targets of space exploration. High-quality gravity field data are significant to refine the physical characterization of Mercury in planetary exploration missions. However, Mercury’s gravity model is limited by relatively low spatial resolution and stripe noises, preventing fine-scale analysis and applications. By analyzing Mercury’s gravity data and topography data in the 2D spatial field, we find they have fairly high spatial structure similarity. Based on this, in this paper, a novel convolution neural network (CNN) approach is proposed to improve the quality of Mercury’s gravity field data. CNN can extract the spatial structure features of gravity data and construct a nonlinear mapping between low- and high-degree data directly. From a low-degree gravity input, the corresponding initial high-degree result can be obtained. Meanwhile, the structure characteristics of the high-resolution digital elevation model (DEM) are extracted and fused to the initial data, to get the final stripe-free result with improved resolution. Given the paucity of Mercury’s data, high-quality lunar datasets are employed as pretraining data after verifying the spatial similarity between gravity and terrain data of the Moon. The HgM007 gravity field obtained by the MErcury Surface, Space ENvironment, GEochemistry and Ranging (MESSENGER) mission at Mercury is selected for experiments to test the ability of the proposed algorithm to remove the stripes caused by quality differences of the highly eccentric orbit data. Experimental results show that our network can directly obtain stripe-free and higher-degree data via inputting low-degree data and implicitly assuming a lunar-like relation between crustal density and porosity. Albeit the CNN-based method cannot be sensitive to subsurface features not present in the initial dataset, it still provides a new perspective for the gravity field refinement. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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15 pages, 4134 KiB  
Technical Note
Long-Distance 3D Reconstructions Using Photogrammetry with Curiosity’s ChemCam Remote Micro-Imager in Gale Crater (Mars)
by Gwénaël Caravaca, Stéphane Le Mouélic, William Rapin, Gilles Dromart, Olivier Gasnault, Amaury Fau, Horton E. Newsom, Nicolas Mangold, Laetitia Le Deit, Sylvestre Maurice, Roger C. Wiens and Nina L. Lanza
Remote Sens. 2021, 13(20), 4068; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204068 - 12 Oct 2021
Cited by 6 | Viewed by 2429
Abstract
The Mars Science Laboratory rover Curiosity landed in Gale crater (Mars) in August 2012. It has since been studying the lower part of the 5 km-high sedimentary pile that composes Gale’s central mound, Aeolis Mons. To assess the sedimentary record, the MSL team [...] Read more.
The Mars Science Laboratory rover Curiosity landed in Gale crater (Mars) in August 2012. It has since been studying the lower part of the 5 km-high sedimentary pile that composes Gale’s central mound, Aeolis Mons. To assess the sedimentary record, the MSL team mainly uses a suite of imagers onboard the rover, providing various pixel sizes and fields of view from close to long-range observations. For this latter, we notably use the Remote Micro Imager (RMI), a subsystem of the ChemCam instrument that acts as 700 mm-focal length telescope, providing the smallest angular pixel size of the set of cameras on the Remote Sensing Mast. The RMI allows observations of remote outcrops up to a few kilometers away from the rover. As retrieving 3D information is critical to characterize the structures of the sedimentary deposits, we describe in this work an experiment aiming at computing for the first time with RMI Digital Outcrop Models of these distant outcrops. We show that Structure-from-Motion photogrammetry can successfully be applied to suitable sets of individual RMI frames to reconstruct the 3D shape and relief of these distant outcrops. These results show that a dedicated set of observations can be envisaged to characterize the most interesting geological features surrounding the rover. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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12 pages, 5560 KiB  
Technical Note
Visual Localization of the Tianwen-1 Lander Using Orbital, Descent and Rover Images
by Wenhui Wan, Tianyi Yu, Kaichang Di, Jia Wang, Zhaoqin Liu, Lichun Li, Bin Liu, Yexin Wang, Man Peng, Zheng Bo, Lejia Ye, Runzhi Wang, Li Yin, Meiping Yang, Ke Shi, Ximing He, Zuoyu Zhang, Hui Zhang, Hao Lu and Shuo Bao
Remote Sens. 2021, 13(17), 3439; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173439 - 30 Aug 2021
Cited by 28 | Viewed by 3884
Abstract
Tianwen-1, China’s first Mars exploration mission, was successfully landed in the southern part of Utopia Planitia on 15 May 2021 (UTC+8). Timely and accurately determining the landing location is critical for the subsequent mission operations. For timely localization, the remote landmarks, selected from [...] Read more.
Tianwen-1, China’s first Mars exploration mission, was successfully landed in the southern part of Utopia Planitia on 15 May 2021 (UTC+8). Timely and accurately determining the landing location is critical for the subsequent mission operations. For timely localization, the remote landmarks, selected from the panorama generated by the earliest received Navigation and Terrain Cameras (NaTeCam) images, were matched with the Digital Orthophoto Map (DOM) generated by high resolution imaging camera (HiRIC) images to obtain the initial result based on the triangulation method. Then, the initial localization result was refined by the descent images received later and the NaTeCam DOM. Finally, the lander location was determined to be (25.066°N, 109.925°E). Verified by the new orbital image with the lander and Zhurong rover visible, the localization accuracy was within a pixel of the HiRIC DOM. Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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11 pages, 4859 KiB  
Technical Note
Photometric Correction of Chang’E-1 Interference Imaging Spectrometer’s (IIM) Limited Observing Geometries Data with Hapke Model
by Xuesen Xu, Jianjun Liu, Dawei Liu, Bin Liu and Rong Shu
Remote Sens. 2020, 12(22), 3676; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223676 - 10 Nov 2020
Cited by 7 | Viewed by 2176
Abstract
The main objective of this study is to develop a Hapke photometric model that is suited for Chang’E-1 (CE-1) Interference Imaging Spectrometer (IIM) data. We first divided the moon into three areas including ‘maria’, ‘new highland’ and old ‘highland’ with similar photometry characteristic [...] Read more.
The main objective of this study is to develop a Hapke photometric model that is suited for Chang’E-1 (CE-1) Interference Imaging Spectrometer (IIM) data. We first divided the moon into three areas including ‘maria’, ‘new highland’ and old ‘highland’ with similar photometry characteristic based on the Hapke parameters of the moon derived from Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) multispectral data. Then, we selected the sample data in the ‘maria’ area and obtained a new set of Hapke model’s parameters that can best fit these data. Result shows that photometric correction using Hapke model with these new derived parameters can eliminate the effect of variations in viewing and luminating geometry, especially ‘opposition surge’, more efficiently than the empirical model. The corrected mosaic shows no significant artifacts along the tile boundaries and more detailed information of the image can be exhibited due to a better correction of ‘opposition surge’ at small phase angle (g < 15°). Full article
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)
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