Seamless 3D Image Mapping and Mosaicing of Valles Marineris on Mars Using Orbital HRSC Stereo and Panchromatic Images

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Article Seamless 3D Image Mapping and Mosaicing of Valles Marineris on Mars Using Orbital HRSC Stereo and Panchromatic Images

Yu Tao 1,* , Greg Michael 2, Jan-Peter Muller 1 , Susan J. Conway 3 and Alﬁah R. D. Putri 1,4

1 Imaging Group, Mullard Space Science Laboratory, Department of Space and Climate Physics, University College London, Holmbury St Mary, Surrey RH5 6 NT, UK; [email protected] (J.-P.M.); alﬁ[email protected] (A.R.D.P.) 2 Department of Geosciences, Institute for Geological Sciences, Planetary Sciences and Remote Sensing, Freie Universität Berlin, 12249 Berlin, Germany; [email protected] 3 Laboratoire de Planétologie et Géodynamique, CNRS, UMR 6112, Université de Nantes, 44300 Nantes, France; [email protected] 4 Department of Atmospheric and Planetary Sciences, Sumatera Institute of Technology (ITERA), Lampung 35365, Indonesia * Correspondence: [email protected]

Abstract: A seamless mosaic has been constructed including a 3D terrain model at 50 m grid-spacing and a corresponding terrain-corrected orthoimage at 12.5 m using a novel approach applied to ESA Mars Express High Resolution Stereo Camera orbital (HRSC) images of Mars. This method consists of blending and harmonising 3D models and normalising reflectance to a global albedo map. Eleven HRSC image sets were processed to Digital Terrain Models (DTM) based on an opensource stereo photogrammetric package called CASP-GO and merged with 71 published DTMs from the HRSC Citation: Tao, Y.; Michael, G.; team. In order to achieve high quality and complete DTM coverage, a new method was developed Muller, J.-P.; Conway, S.J.; to combine data derived from different stereo matching approaches to achieve a uniform outcome. Putri, A.R.D. Seamless 3D Image This new approach was developed for high-accuracy data fusion of different DTMs at dissimilar Mapping and Mosaicing of Valles grid-spacing and provenance which employs joint 3D and image co-registration, and B-spline fitting Marineris on Mars Using Orbital against the global Mars Orbiter Laser Altimeter (MOLA) standard reference. Each HRSC strip is HRSC Stereo and Panchromatic normalised against a global albedo map to ensure that the very different lighting conditions could be Images. Remote Sens. 2021, 13, 1385. corrected and resulting in a tiled set of seamless mosaics. The final 3D terrain model is compared https://doi.org/10.3390/rs13071385 against the MOLA height reference and the results shown of this intercomparison both in altitude

Academic Editors: Stephan van and planum. Visualisation and access mechanisms to the ﬁnal open access products are described. Gasselt and Andrea Nass Keywords: 3D reconstruction; stereo; co-registration; 3D co-alignment; DTM; mosaic; HRSC; Valles

Received: 22 March 2021 Marineris; CASP-GO Accepted: 1 April 2021 Published: 3 April 2021

Publisher’s Note: MDPI stays neutral 1. Introduction with regard to jurisdictional claims in Valles Marineris on Mars are the largest system of canyons in the Solar System. The published maps and institutional affil- system is more than 4,000 km long, 200 km wide, and up to 10 km deep with respect iations. to the surrounding plateaus. Because of their sheer size Valles Marineris have been a subject of growing number of studies since the Viking era and we now know that they have been subject to a wide variety of processes throughout their history. Estimated to be approximately 3.5 Ga old [1], the superstructure is related to tectonic rifting of the Copyright: © 2021 by the authors. crust related in part to the loading induced by the Tharsis Volcanic province [1–4]. This Licensee MDPI, Basel, Switzerland. overall tectonic structure has been modified by outflows [5–8], fluvial processes [7,9–11] This article is an open access article (delta, melas), volcanic processes [12–14], aeolian processes [15–17] and it also thought to distributed under the terms and contain evidence of glacial modification [18,19]. Its extreme topographic relief influences conditions of the Creative Commons the atmospheric circulation and creates weather systems unique to the canyon [20,21]. Vast Attribution (CC BY) license (https:// sediment accumulations have formed on its floor, whose geochemical signature attests creativecommons.org/licenses/by/ to standing bodies of water in the past [9,21–24]. Giant rock avalanches have affected 4.0/).

Remote Sens. 2021, 13, 1385. https://doi.org/10.3390/rs13071385 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 1385 2 of 26

the walls throughout its history, e.g., [25–28], and whose study has been influential in understanding landslides throughout the Solar System [29–31]. The Valles Marineris also host a particularly high concentration of Recurring Slope Lineae (RSL, e.g., [32]), which could be signs of liquid water seeps at the present-day [33]. These low albedo streaks are diminutive compared to the canyon, being that they usually only attain hundreds of metres in length for metres to tens of metres in width. Their downslope growth during the warmest times of year and annual fading strongly suggests the involvement of liquid water, which is usually unstable under current Martian conditions. Studying the examples in Valles Marineris has been key to understanding the conditions required for RSL growth and their seasonality with different slope expositions [32,34–36]. 3D mapping is essential to improving our understanding of the geology and geomorphology of Valles Marineris. A key aspect of this mapping is to construct a base-map of co-aligned 3D and mosaiced terrain-corrected images covering the area. One of the options is to employ the Mars Orbiter Laser Altimeter (MOLA) Digital Terrain Model (DTM) [37] which provides global 3D information of Mars at a resolution of 463 m/pixel. However, the inter-track spacing of MOLA tracks is ~4 km at the equator so such a DTM contains many interpolated points. While the MOLA DTM at 463 m/gridpoint is considered to be the most precise topographic map of Mars to date and provides nearly 100% coverage of the planet, we believe the better option is to exploit co-aligned High Resolution Stereo Camera (HRSC) [38] stereo products at a much higher spatial resolution. HRSC is now on its 20,000+ orbits aboard Mars Express imaging and has covered ~98% of the Martian surface at a spatial resolution higher than 100 m/pixel. The German Aerospace Centre (Deutsches Zentrum für Luft- und Raumfahrt; DLR) processes HRSC stereo images to level 4 (see http://europlanet.dlr.de/fileadmin/pictures/hrsc/HRSC_Fact_Sheet_V5 .pdf (accessed on 21 March 2021)) DTMs, at 50–150 m/pixel resolution, which now cover ~50% of the planet’s surface. At Valles Marineris, the DLR HRSC level 4 strip DTMs cover a large proportion of the canyons, leaving only a few gaps of unprocessed or not acquired orbital strip data. However, it should be noted that Valles Marineris also suffers from frequent fog events, obscuring part of the valley bottom, making the DTM production process difficult for some of the single-strip stereo images at this area. In this work, we filled the gaps in the pre-existing DLR-generated HRSC products by creating a further 11 HRSC single-strip DTMs and Ortho-Rectified Images (ORIs), hereafter referred to University College London (UCL) level 4 DTMs/ORIs, using HRSC level 2 (radiometrically-calibrated but not map-projected; http://europlanet.dlr.de/fileadmin/ pictures/hrsc/HRSC_Fact_Sheet_V5.pdf (accessed on 21 March 2021)) stereo images. These UCL level 4 DTMs and ORIs were then co-registered with the existing DLR HRSC level 4 DTMs and ORIs to allow seamless mosaicing of the whole of Valles Marineris. A key component is to ensure that all DTMs have a common reference system, which in our case is the MOLA DTM. The UCL and DLR HRSC level 4 DTMs are co-aligned with the MOLA DTM to provide precise topographic information for future geological studies. The 3D reconstruction pipeline employed in our work is a hybrid version of CASP- GO, which was developed within the completed EU FP-7 iMars (http://www.i-mars.eu (accessed on 21 March 2021)) project for NASA Mars Reconnaissance Orbiter (MRO) Con- TeXt camera (CTX) and High Resolution Imaging Science Experiment (HiRISE) stereo images. The UCL CASP-GO DTM system was previously used to produce planet-wide CTX DTMs [39]. In this work, we improve the original CASP-GO CTX pipeline to produce optimised HRSC DTMs. Moreover, rather than employ bundle block adjustment, which requires specialist photogrammetry skills and processing of a bundle of data together, we employ a more brute force approach here given that a global reference 3D model and existing DLR level 4 products exist. This is performed using a method familiar from terres- trial laser scanning which employs 3D point cloud alignment and is performed in parallel with feature-based matching of images to ensure global congruency. After processing overlapping 3D information and co-registering overlapping images with existing HRSC Remote Sens. 2021, 13, 1385 3 of 26

level 4 DTMs, ORIs, and MOLA data, we have been able to build a seamless HRSC DTM mosaic that covers the whole of Valles Marineris. Finally, HRSC level 4 and the newly processed ORIs are Lambertian reflectance corrected and brightness-referenced to a global albedo map produced by the Thermal Emission Spectrometer (TES) on the Mars Global Surveyor (MGS) [40]. This allows us to bring the HRSC image strips into mutual consistency of absolute surface brightness despite variations in atmospheric optical scattering at different observation times to achieve a near-seamless mosaic [41]. The rest of this paper is structured as follows. In Section 1.1, we review the datasets employed and the relevant previous work follows in Section 1.2. In Section 2.1, details of the input and reference data are provided. From Sections 2.2–2.6, we describe technical details step-by-step for all the methods that are involved in this work, in the order of single- strip HRSC DTM production, HRSC-to-HRSC and HRSC-to-MOLA 3D co-alignments and mosaicing, HRSC ORI production, and subsequent photometric correction and mosaicing of ORIs. In particular, Section 2.2 reviews the CASP-GO pipelines which are used as backbone 3D mapping software in this work. In Section 2.3, we introduce the hybrid HRSC DTM processing chain for single-strip HRSC DTM production, followed in Section 2.4, by an introduction into the joint 3D and image co-registration processing chain for HRSC- to-HRSC and HRSC-to-MOLA co-alignment. In Section 2.5, we introduce the automated image co-registration and orthorectification method, followed in Section 2.6, with the ORI photometric correction and subsequent mosaicing. The final resultant DTM and ORI mosaic are shown in Section3, along with quality and accuracy evaluations, and data access information. Issues or limitations of the proposed methods and resultant products are discussed in Section4. Finally, in Section5, we summarise the work presented and examine some possibilities for the future.

1.1. Datasets In this work, we use the MOLA areoid (https://pds-geosciences.wustl.edu/missions/ mgs/megdr.html (accessed on 21 March 2021)) data as our baseline reference system (both spatially and vertically). MOLA (from 1997 to 2001) was one of the five instruments onboard MGS that measured the time-of-flight of laser pulses at 1064 nm, over the distance from the spacecraft to the Martian surface. Individual MOLA laser tracks were interpolated and extrapolated, and corrected, by the MOLA team to produce a global DTM of Mars at 463 m grid spacing [37], which is available through the NASA Planetary Database System (PDS; https://pds-geosciences.wustl.edu/missions/mgs/megdr.html (accessed on 21 March 2021)). The HRSC stereo imaging instrument onboard the ESA’s Mars Express (MeX) spacecraft is still orbiting Mars having acquired its first images in January 2004. HRSC was designed for photogrammetric mapping of the topography of the Martian surface via continuous acquisition of stereo push-broom images from multiple angles on a single orbital pass. HRSC captures multi-angular and multi-colour images mostly at 12.5–100 m pixel- scale over various swath-widths, providing near global (~98%) coverage of the Martian surface at 12.5–100 m/pixel. The DLR processed HRSC level 4 DTMs cover as of the time of submission ~50% of the Martian surface. In this work, we use the HRSC level 2 stereo images for UCL level 4 DTM processing and the DLR HRSC level 4 DTMs (version 50 and higher) [42] for co-registration, co-alignment with MOLA, and mosaicing. It should be noted that only the nadir image is captured at 12.5 m with the off-nadir stereo channels usually captured 2 times coarser at 25 m.

1.2. Previous Work In [39], we previously compared the quality and performance of CTX DTMs produced using different existing planetary 3D reconstruction software and introduced the CASP-GO system, which is based on the open-source NASA Ames Stereo Pipeline (ASP) [43], tie-point based multi-resolution image co-registration [44], the Gotcha [45] sub-pixel reﬁnement Remote Sens. 2021, 13, 1385 4 of 26

method, and other optimisation algorithms. The CASP-GO system produces high quality DTMs in terms of improved completeness and accuracy, as well as minimising matching and interpolation artefacts. For HRSC data, DLR generate HRSC single-strip DTMs [42] in a sinusoidal projection system at 50–150 m grid-spacing for level-4 products. More recently, DLR have developed a level-5 processing chain to produce a large (>100 strip) HRSC DTM mosaics with the Leibniz University of Hanover and Free University of Berlin (FUB) for several United States Geological Survey (USGS) quadrangles (http://hrscteam.dlr.de/HMC30/ (accessed on 21 March 2021)) including the MC11 (0–30◦ N, 0–45◦ W) map quadrangle in an Equidistant Cylindrical projection system at 50 m grid spacing. For the Valles Marineris area, which is mainly in the MC18 (0–30◦ S, 45◦–90◦ W) quadrangle and partially in the MC17 (0–30◦ S, 90◦–135◦ W) and MC19 (0–30◦ S, 0–45◦ W) quadrangles, there are 71 pre-existing DLR HRSC level 4 single-strip DTMs available on the ESA-PSA site. DLR processing of the raw HRSC level-2 data includes radiometric de-calibration, noise reduction, image matching, geo-referencing, photogrammetric processing and along- track Bundle Adjustment (BA). The ﬁnal products are labelled as “Level-4 v50+” when they reach a satisfactory level of quality [42]. The co-registration of HRSC DTM and MOLA DTM is checked using average height differences and usually reveal residual horizontal offsets smaller than the grid spacing of the HRSC DTM and residual vertical offsets below 10 m [42]. In addition, bundle block adjustment and joint interpolation of multi-scale 3D point data sets are used for DTM mosaicing. For the ORI mosaic, [46] describes the standard DLR method using single strip scaling (to calibrated radiances), brightness normalisation according to Lambert reﬂection, and local contrast adjustment and brightness adjustment to the MGS’s TES albedo [39,41]. Another related work on wide area HRSC DTM/ORI production and mosaicing is [47], in which the authors demonstrated the production of a HRSC DTM and ORI mosaic for the south polar region using the DLR/NASA VICAR (https://www-mipl.jpl.nasa.gov/vicar_ os/v1.0/vicar-docs/VICAR_guide_1.0.pdf (accessed on 21 March 2021)) based pipeline and brightness correction through a high altitude HRSC reference image as well as the use of the 128 m/pixel MOLA DTM to provide height normalisation. In this previous work, individual HRSC orbital strips were separately bundle-adjusted by FUB due to little coverage of DLR HRSC products [47].

2. Materials and Methods 2.1. Data Preparation For HRSC, 71 DLR processed HRSC level 4 DTMs and ORIs were available until April 2020. These include 57 with DTMs at 50 m/pixel and ORIs at 12.5 m/pixel; 6 with DTMs at 100 m/pixel and ORIs at 25 m/pixel and 8 with DTMs at 150 m/pixel and ORIs at 50 m/pixel. A list of the image IDs with download links are provided as Supplementary Material. The 71 DLR HRSC level 4 DTMs cover most of the Valles Marineris region but leave several signiﬁcant gaps. Therefore, we checked all available HRSC level 2 images and selected a set of 11 HRSC level 2 stereo pairs at 12.5 m/pixel to be processed to DTMs and mosaiced at 50 m/pixel. The corresponding level 2 HRSC orbit IDs are h2112_0000, h2123_0000, h2134_0000, h2211_0000, h2145_0000, h2178_0000, h1896_0000, h0438_0000, h3195_0000, h2182_0000, h2402_0001. The image footprints of the HRSC DLR products and UCL products are shown in Figure1. Remote Sens. 2021, 13, 1385 5 of 26 Remote Sens. 2021, 13, x FOR PEER REVIEW 5 of 28

FigureFigure 1.1. Image footprints footprints showing showing coverage coverage of th ofe DLR the DLRHRSC HRSC level 4 levelproducts 4 products and level and 2 images level (UCL 2 images processed (UCL level processed 4 products) level 4at products) Valles Marineris. at Valles Marineris. 2.2. The ASP and CASP-GO Auto-DTM Generation System 2.2. The ASP and CASP-GO Auto-DTM Generation System TheThe Ames Ames StereoStereo Pipeline (hereafter (hereafter referre referredd to as to ASP) as ASP)software software was developed was developed by bythe the Intelligent Intelligent Robotics Robotics Group Group at the at NASA the NASA Ames AmesResearch Research Center, Mountain Center, Mountain View, CA View, CA[43]. [43 ].ASP ASP is a is suite a suite of open-source of open-source automa automatedted geodesy geodesy and stereo-photogrammetry and stereo-photogrammetry tools toolsdesigned designed for processing for processing planetary planetary imagery imagery captured captured either from either planetary from (robotic) planetary orbit- (robotic) orbitersers or orlanders landers or from or from Earth Earth observing observing satellites. satellites. The CASP-GO The CASP-GO pipeline pipelineis based on is basedthe on theASP ASP framework framework with with specific specific enhancements, enhancements, dealing dealing with matching with matching artefacts, artefacts,gaps, and gaps, andco-alignment co-alignment issues, issues, from fromin-house in-house software software previously previously developed developed at the Imaging at theGroup Imaging Groupat UCL. at UCL. CASP-GO CASP-GO uses the uses ASP the I/O ASP interface I/O interface and takes and USGS takes Integrated USGS Integrated Software Softwarefor forImagers Imagers and and Spectrometers Spectrometers (USGS-ISIS) (USGS-ISIS) formatte formattedd stereo stereoimages images(see Figure (see 2; Figure see expla-2; see ex- nation of the USGS-ISIS Cube format in https://isis.astrogeology.usgs.gov/docu- planation of the USGS-ISIS Cube format in https://isis.astrogeology.usgs.gov/documents/ ments/LogicalCubeFormatGuide/LogicalCubeFormatGuide.html (accessed on 21 March LogicalCubeFormatGuide/LogicalCubeFormatGuide.html (accessed on 21 March 2021)) 2021)) and a reference base map as inputs. Converting from raw HRSC PDS data into the andUSGS-ISIS a reference formatted base map Cube as inputs.includes Converting the process fromof format raw HRSCconversion, PDS dataimage into pre-de- the USGS- ISISnoising, formatted SPICE Cube (see includeshttps://naif.jpl.nasa.gov the process of/naif/spiceconcept.html format conversion, image (accessed pre-denoising, on 21 March SPICE (see2021)) https://naif.jpl.nasa.gov/naif/spiceconcept.html kernel initialisation, and map projection processes (accessed (see https://isis.astrogeol- on 21 March 2021)) ker- nelogy.usgs.gov/Application/index.html#Mars_Expr initialisation, and map projection processesess). (see The https://isis.astrogeology.usgs.gov/ core matching algorithm uses Application/index.html#Mars_Expressa combination of cross-correlation and a 5 (accessed th generation on 21of adaptive March 2021)). least squares The core correla- matching algorithmtion (ALSC) uses and acombination region growing of matcher cross-correlation called Gotcha and [45], a5 which th generation provides accurate of adaptive and least squaresrobust correlation sub-pixel conjugate (ALSC) andpoints. region growing matcher called Gotcha [45], which provides accurateThe and complete robust CASP-GO sub-pixel workflow conjugate (see points. Figure 2) for DTM production includes the followingThe complete 10 steps CASP-GO which are shown workflow in the (see aforementioned Figure2) for figure DTM and production described includes as fol- the lows: (a) ASP image pre-processing including image enhancement (e.g., Laplacian of following 10 steps which are shown in the aforementioned figure and described as follows: Gaussian filtering and image normalisation) and stereo rectification; (b) ASP disparity (a) ASP image pre-processing including image enhancement (e.g., Laplacian of Gaussian map initialisation using coarse cross-correlation; (c) UCL fast Maximum Likelihood (f- filteringML) matching and image and normalisation) the construction and of a stereo “float” rectification; initial disparity (b) ASPmap; disparity(d) ASP Bayes map Ex- initialisa- tionpectation using coarse Maximisation cross-correlation; (EM) weighted (c) UCL affine fast adaptive Maximum sub-pixel Likelihood cross-co (f-ML)rrelation; matching (e) and theUCL construction re-defined of outlier a “float” rejection initial and disparity gap erosion map; scheme (d) ASP to detect, Bayes Expectationremove and eliminate Maximisation (EM) weighted affine adaptive sub-pixel cross-correlation; (e) UCL re-defined outlier rejection and gap erosion scheme to detect, remove and eliminate mis-matched and unreliable disparity values; (f) UCL ALSC sub-pixel refinement for previous matches; (g) UCL Gotcha

based densiﬁcation method for ﬁlling-in disparity gaps; (h) ASP camera triangulation and DTM creation; (i) UCL co-kriging grid-point interpolation and calculation of height un- Remote Sens. 2021, 13, x FOR PEER REVIEW 6 of 28

Remote Sens. 2021, 13, 1385 6 of 26 mis-matched and unreliable disparity values; (f) UCL ALSC sub-pixel refinement for previous matches; (g) UCL Gotcha based densification method for filling-in disparity gaps; (h) ASP camera triangulation and DTM creation; (i) UCL co-kriging grid-point interpola- tioncertainties and calculation for interpolations; of height (j) uncertainties UCL ORI co-registration/geocoding for interpolations; (j) UCL with ORI reference co-registra- to the tion/geocodinginput base map with and DTMreference adjustment. to the inpu A detailedt base map description and DTM of each adjustment. of these processingA detailed descriptionsteps can be of found each in of [ 39these]. This processing processing steps chain can operates be found without in [39]. any This user processing interaction chain and wasoperates shown without in [39] any to be user sufﬁciently interaction robust and thatwas itshown worked in without[39] to be any sufficiently manual intervention robust that itfor worked over 5000 without CTX stereo-pairs.any manual intervention for over 5,000 CTX stereo-pairs.

Figure 2. Flow diagram of the UCL CASP-GO auto-DTM processing system. Grey indicates the ASP system, green for UCL Figure 2. Flow diagram of the UCL CASP-GO auto-DTM processing system. Grey indicates the ASP system, green for UCL processing, and pink for USGS-ISIS processing. processing, and pink for USGS-ISIS processing. 2.3. The Hybrid Stereo Processing Chain 2.3. The Hybrid Stereo Processing Chain As previously mentioned in [39], different DTMs from different stereo pipelines alwaysAs displaypreviously different mentioned kinds in of [39], matching different artefacts DTMs from or gaps. different Although stereo thesepipelines can beal- partiallyways display reduced different with kinds down-sampling of matching and artefacts interpolation, or gaps. Although minimising these matching can be artefactspartially reducedand better with matching down-sampling coverage and are interpolation, the keys to obtain minimising a high qualitymatching DTM artefacts with and the bestbet- possibleter matching resolution coverage (finest are details). the keys to obtain a high quality DTM with the best possible resolutionThe quality (finest ofdetails). the final disparity map used for camera triangulation dominates the qualityThe of quality the final of the DTM final and disparity is mostly map determined used for camera by the triangulation matching blocks dominates in a DTM the pipeline.quality of the As final the stereo DTM and matching is mostly algorithms determined have by become the matching more blocks mature in over a DTM the pipe- past line.decade, As the practical stereo matchingmatching issuesalgorithms mostly have come become from more one ormature more over natural the issuespast decade, of the practicalstereo inputs, matching for example, issues mostly robustness come to from illumination one or more differences, natural noiseissues levelof the differences, stereo inputs,changes for orexample, degradations robustness in surface to illumination textures, differences, a good stereo noise convergence level differences, angle, changes and the ordifferences degradations in their in surface native resolutionstextures, a good (although stereo raw convergence images are angle, generally and resampledthe differences to a infixed their resolution native resolutions in planetary (although imaging, raw the images individual are generally image clarity resampled still varies to a fixed within reso- the same instrument over the same region). How well a stereo matching algorithm can handle these different types of input issues ultimately determines the quality of the resulting DTM. Although some matching algorithms are more robust to bad quality inputs or differences

Remote Sens. 2021, 13, 1385 7 of 26

in image conditions, there is not a single algorithm that can handle all different types of input issues. In particular, the ASP Bayes EM optimised cross-correlation matching method is a collection of algorithms that are based on image cross-correlation but has been augmented and tuned in efforts to produce more accurate results and obtain robustness over many different input issues. These optimisations, on top of the general “cross-correlation” method, include a coarse-to-fine (pyramidal) approach, filtering, segmentation (tiling), searching- range optimisation that are based on pre-processing feature matching, and finally the Bayes EM subpixel refinement to adapt to affine and noise variations. The ASP Bayes EM optimised cross-correlation matching method has shown robustness to image noise and good performance on semi-low to highly textured areas, but it is more likely to be affected by the differences in local lighting conditions and contrast, and has poor performance on very-low to non-textured regions (e.g., strongly shaded regions). Also, the ASP Bayes EM optimised cross-correlation matching method is generally robust to many different planetary observation instruments but has some inherent issues from the local (window-based) matching methods, such as block (staircase) artefacts. On the other hand, the ASP More Global Matching (MGM) method is based on the semi-global matching (SGM) algorithm [48] and the original MGM algorithm that is described in [49]. MGM has been optimised during its implementation in ASP, in terms of 2 D disparity search (instead of 1 D search), multi-resolution hierarchical search, and smoothing of high frequency artefacts for textureless regions. The ASP MGM based matching method has shown less robustness to different instruments (compared to Bayes EM optimised cross-correlation) but has very high matching accuracy and good performance on textureless regions as well as steep slopes. Also, the ASP MGM based matching method has shown higher robustness to handling differences in lighting conditions and contrasts, however, it may produce seam artefacts and diagonal artefacts for large (continuous) low textured regions. Finally, the UCL Gotcha matcher [45], which was introduced in [39] as a complementary matching algorithm to the ASP Bayes EM optimised cross-correlation matching method, starts from a group of MSA-SIFT “corner” features and gradually grows to their neighbouring pixels with local constraints. The Gotcha matcher is not suitable for global matching due to its high computational cost but is able to produce high accuracy matching results for small images/regions. The Gotcha matcher often produces over-smoothed results on low textured regions, but on the other hand, has shown robustness to local illumination differences, perspective degradations, and a sub-optimal stereo convergence angle. Also, there is no known systematic artefact from Gotcha [45] but it requires a clean (artefact-free) initial matching result as input (from tie-points or other stereo matching disparity maps). In this work, we experimented with a hybrid method based on CASP-GO and the ASP’s new MGM matcher in order to try to exploit the strengths of different matching algorithms whilst minimising the artefacts to produce the best possible quality DTM. The three matching algorithms in the proposed hybrid method contain two local methods and one global method. They provide solutions for different conditions and difficult regions of HRSC stereo images. Although it may not always be feasible to have three parallel algorithms in a general 3D mapping task, the proposed hybrid method has demonstrated better overall performance compared to the three methods alone. The proposed HRSC hybrid DTM processing chain follows the standard USGS- ISIS/ASP data I/O methods and takes ISIS formatted HRSC level 2 stereo images as inputs. The same ASP pre-processing methods that were described in [43] and more specifically in [39] are also used. The complete HRSC hybrid DTM processing chain, is shown in Figure3, and has the following 6 steps, replacing the original steps (a) to (g) in Figure2: (a) ASP stereo pre- processing (image normalisation, Laplacian of Gaussian filtering, initial feature matching to decide a searching range); (b) application of the fast-Maximum Likelihood (f-ML) matching Remote Sens. 2021, 13, x FOR PEER REVIEW 8 of 28

The complete HRSC hybrid DTM processing chain, is shown in Figure 3, and has the following 6 steps, replacing the original steps (a) to (g) in Figure 2: (a) ASP stereo pre- processing (image normalisation, Laplacian of Gaussian filtering, initial feature matching Remote Sens. 2021, 13, 1385 to decide a searching range); (b) application of the fast-Maximum Likelihood8 of 26(f-ML) matching to build a “float” initial disparity map (see [39] for more details) and then application of the ASP Bayes EM weighted affine adaptive sub-pixel cross-correlation matching to torefine build the a “float” disparity initial map; disparity (c) in map parallel (see [39 to] for (b), more Gotcha details) matcher and then is applicationapplied based of the on an initialASP disparity Bayes EM map weighted created affine from adaptive the ASP sub-pixel Normalised cross-correlation Cross-Corr matchingelation to (NCC) refine themethod; (d)disparity in parallel map; to (b) (c) inand parallel (c), the to ASP (b), Gotcha MGM matcher matcher is is applied directly based applied on an to initial the pre-processed disparity stereomap inputs; created from(e) application the ASP Normalised of a slightly Cross-Correlation stricter outlier (NCC) filtering method; (d)and in border parallel toerosion scheme(b) and to (c), remove the ASP potential MGM matcher bad-matches, is directly as applied we have to the three pre-processed disparity stereomaps inputs;from three matching(e) application methods of a in slightly parallel; stricter (f) finally, outlier filtering the three and disparity border erosion maps scheme are blended to remove together usingpotential a “closest-to-median” bad-matches, as we scheme have three (described disparity in maps the next from paragraph). three matching Note methods that the in clos- parallel; (f) finally, the three disparity maps are blended together using a “closest-to-median” est-to-median blending is able to discard a bad-match by considering the three matching scheme (described in the next paragraph). Note that the closest-to-median blending is resultsable toand discard neighbouring a bad-match disparities by considering but is hi theghly three dependent matching resultson (e) to and discard neighbouring the majority of bad-matches.disparities but is highly dependent on (e) to discard the majority of bad-matches.

Figure 3. Flow diagram of the HRSC hybrid DTM processing chain using a combination of cross-correlation (ASP), Gotcha Figure 3. Flow(UCL), diagram and MGM of (ASP).the HRSC Note hybrid that this DTM flow diagramprocessing expands chain step usin (a)g toa (g)combination of Figure2. of cross-correlation (ASP), Gotcha (UCL), and MGM (ASP). Note that this flow diagram expands step (a) to (g) of Figure 2. The closest-to-median blending scheme can be described in the following steps: (a) for anyThe given closest-to-median pixel at (x,y), we haveblending three scheme X-direction can disparity be described values, in denoted the following as dx_1(x,y), steps: (a) fordx_2(x,y), any given dx_3(x,y), pixel andat (x,y), three Y-directionwe have disparitythree X-direction values, denoted disparity as dy_1(x,y), values, dy_2(x,y), denoted as dx_1(x,y),dy_3(x,y), dx_2(x,y), from the threedx_3(x,y), disparity and maps; thr (b)ee SortY-direction the three xdisparity disparity valuesvalues, into denoted each as dy_1(x,y),of their 8dy_2(x,y), adjacent (neighbouring) dy_3(x,y), from disparity the thr values.ee disparity Note that maps; any (b) of the Sort disparity the three values x dispar- and their 8 nearest neighbours could be “Nodata” if the matcher fails at that location or ity values into each of their 8 adjacent (neighbouring) disparity values. Note that any of at a neighbouring location. “Nodata” is not counted in later steps; (c) Forcibly remove the4 disparity “outliers” values that are and present their at 8 thenearest two endsneighb ofours the sorted could vector be “Nodata” from (b) if and the calculate matcher fails at thata median; location (d) or Any at ofa neighbouring the three X-direction location. disparity “Nodata” values, is not i.e. counted dx_1(x,y), in dx_2(x,y),later steps; (c) Forciblydx_3(x,y), remove that are 4 “outliers” closest to the that median are present from (c) at is the set two as the ends final of blended the sorted disparity vector value. from (b) andIf anycalculate of them a median; is “Nodata” (d) or Any was of removed the three at (c), X-direction they will not disparity be counted values, in this i.e. step. dx_1(x,y), If dx_2(x,y),all three ofdx_3(x,y), dx_1(x,y), that dx_2(x,y), are closest dx_3(x,y) to the are median “Nodata” from or were (c) is removed set as the at (c), final the blended median dis- parityvalue value. calculated If any at (c)of willthem be is set “Nodata” as the final or disparity was removed value for at this (c), pixel; they (e) will Repeat not (b)–(d)be counted in thisfor Y-direction step. If all disparities. three of dx_1(x,y), dx_2(x,y), dx_3(x,y) are “Nodata” or were removed at The advantages of using the hybrid matching system are demonstrated in Figure4 for (c), the median value calculated at (c) will be set as the final disparity value for this pixel; 5 different areas that are cropped from one of the HRSC single-strip (h2145_0000) stereo (e) imagesRepeat displaying(b)-(d) for theY-direction X-direction disparities. disparity maps (dx) as examples. These examples show different characteristics of the stereo inputs, including (Area-1) hills and dunes with

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The advantages of using the hybrid matching system are demonstrated in Figure 4

Remote Sens. 2021, 13, 1385 for 5 different areas that are cropped from one of the HRSC single-strip (h2145_0000)9 of 26 stereo images displaying the X-direction disparity maps (dx) as examples. These examples show different characteristics of the stereo inputs, including (Area-1) hills and dunes with illumination differences and high noise-levels; (Area-2) low-textured flat surfaces, cliffs withillumination good contrast, differences and small and high and noise-levels; medium sized (Area-2) craters; low-textured (Area-3) heavily flat surfaces, shaded cliffs regions andwith steep good slopes; contrast, (Area-4) and small sand and dunes medium with sized cont craters;rast differences; (Area-3) heavily (Area-5) shaded bright regions cliffs and and steep slopes; (Area-4) sand dunes with contrast differences; (Area-5) bright cliffs and flat rocks with little contrast differences. The results are shown of the 5 cropped areas from flat rocks with little contrast differences. The results are shown of the 5 cropped areas the input HRSC level 2 stereo image, the X-disparity maps from the 3 different (afore- from the input HRSC level 2 stereo image, the X-disparity maps from the 3Different (afore- described)described) matching algorithmsalgorithms alone,alone, and and the the final final merged merged X-disparity X-disparity map map using using the the proposedproposed hybrid system.system.

Figure 4. Examples of stereo input images and their corresponding resultant x-disparity maps Figureusing the4. Examples 3 matchers of (shownstereo input in Figure images3) standalone and their corresponding (Disparity-1, Disparity-2, resultant x-disparity Disparity-3) maps and usingmerged the using 3 matchers the Hybrid (shown DTM in processingFigure 3) standa schemelone (Disparity-Final), (Disparity-1, Disparity-2, for 5 different Disparity-3) cropped areas, and demonstrated using h2145_0000 (location can be found from Figure 8).

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We can observe (images at original resolution are provided in supplementary data) that, in general, the f-ML + Bayes EM cross-correlation method (Disparity-1) is able to capture finer texture details but has more gaps (failed matching), the NCC + Gotcha method (Disparity-2) shows higher completeness (fewer gaps) but slightly less detail (smoother), while the MGM method (Disparity-3) shows good overall quality and completeness, but with less detail and some artefacts resulting from inherent (un-smoothed) SGM [48] issues (diagonal lines/8-path streaking artefact [49]). Nevertheless, each of the three methods separately are subject to their own advantages and disadvantages given different types of terrains, lighting and contrast conditions/differences, noise, low-textured and high- textured surfaces. The final merged disparity (Disparity-final), using the afore-described “closest-to-median” scheme shows the highest quality, i.e., best overall completeness and level of detail, and the least artefacts.

2.4. DTM Height Correction and Mosaicing Following the successful creation of 11 single-strip HRSC DTMs, the next stage focuses on spatial and vertical alignment of the new HRSC DTMs to existing DLR HRSC DTMs and ultimately to the only global Mars co-ordinate reference, the MOLA DTM. Within the DLR streamlined system, co-alignment of HRSC DTMs and MOLA is achieved using Bundle Adjustment (BA) and Sequential Photogrammetric Adjustment (SPA) [46]. More specifically, BA uses tie-points and least squares adjustment to model systematic and random errors in a single mathematical model to correct the 3D geometry, whereas SPA takes a comprehensive set of adjustment procedures and iteratively refines the 3 D geometry. The SPA procedures [46] include (a) minimising 3D intersection error for individual points; (b) calculation of mean lateral and vertical shifts to MOLA to correct orbit position; (c) calculation of along-track vertical deviations to MOLA to correct pitch angle; and (d) calculation of systematic across-track tilts to MOLA to correct roll angles. In this work, we propose an alternative method to perform HRSC-to-HRSC and HRSC-to-MOLA co-alignment using a joint 3D and image co-registration procedure. The joint 3D and image co-registration process uses existing image and 3D data as references, and corrects a target image and 3D data simultaneously, according to image-to-image co-registration for spatial transformation and 3D-to-3D co-alignment for vertical transformation. This method uses a combination of image co-registration and 3D co-alignment techniques and, we believe, is more intuitive and straightforward than traditional photogrammetric modelling approaches. The joint 3D and image co-registration method also guarantees high co-alignment accuracy both globally and locally. However, it is only applicable when there is partial overlap between the target data and reference data, e.g., adjustment of new HRSC single-strip products to fill in gaps of already corrected HRSC single-strip products. The method can also be used for CTX-to-HRSC, CTX-to- CTX, HiRISE-to-CTX, and HiRISE-to-HiRISE co-alignment and mosaicing. However, for a “blind” HRSC-to-MOLA alignment task (without supporting reference HRSC products), we still suggest following the DLR BA and SPA procedures. The overall processing chain for the joint 3D and image co-registration method is illustrated in Figure5. The joint 3D and image co-registration method takes our processed HRSC ORIs and DTMs, MOLA DTM, and the existing DLR HRSC level 4 v50+ ORIs and DTMs as inputs, corrects both the UCL HRSC level 4 single-strip ORIs and DTMs as well as the existing DLR HRSC single-strip level-4 DTMs, and creates a DTM mosaic for the whole area. This is achieved through the 5 steps shown in Figure5 with annotated labels relating to the following: (a) Firstly, a cropped version of the MOLA DTM is upsampled to 50 m/pixel and mosaiced with the existing DLR HRSC level 4 DTMs for ease of computation. In this step, we have prepared a reference DTM mosaic, for follow-up steps, covering the target DTMs, consisting of both high-resolution regions that come from the original DLR HRSC level 4 DTMs and lower resolution (smoother) regions that were filled with up-sampled MOLA DTMs, together covering the target UCL HRSC level 4 DTMs. Remote Sens. 2021, 13, 1385 11 of 26

(b) Co-registration of the UCL HRSC level 4 ORIs with existing DLR HRSC level 4 ORIs for overlapping regions and recording of the spatial correspondences for all 2 D points (P), here denoted as TP (dX0, dY0), for the next step. The ORI co-registrations are performed using Mutual Shape Adapted Scale Invariant Feature Transform (MSA- SIFT), which was introduced in [44] and was also implemented as part of the CASP-GO pipeline [39]. Note that the reason for using image co-registration prior to 3D co-alignment is that images always have higher spatial resolution with more detailed textures comparing to any derived stereo-derived DTM, and thus result in more accurate spatial adjustments than simply performing a point-cloud alignment using Iterative Closest Point (ICP) based methods. Figure6 shows that the 3D co-alignment results for using point-cloud alignment only and using the proposed joint 3D and image co-registration method. We can see that in both cases, the two HRSC DTMs are “co-aligned” at the DTM scale and could be Remote Sens. 2021, 13, x FOR PEER REVIEW 11 of 28 “seamlessly” blended together. However, when looking at the ORIs, which are at a ﬁner scale of resolution, the differences become much more obvious.

Figure 5. Flow diagram of the joint 3D and image co-registration processing chain for HRSC-to-HRSC and HRSC-to- FigureMOLA 5. Flow co-alignment. diagram of the joint 3 D and image co-registration processing chain for HRSC-to-HRSC and HRSC-to-MOLA co- alignment. (c) Take the spatial correspondences from (b), assuming an initial vertical transforma- (a) Firstly, a cropped version of the MOLA DTM is upsampled to 50 m/pixel and tion equals to the absolute difference of height values for the corresponding 2 D points, P, mosaiced with the existing DLR HRSC level 4 DTMs for ease of computation. In this step, here denoted as dZ , as the initial transformation, i.e., T (dX , dY , dZ ), adding equally we have prepared a0 reference DTM mosaic, for follow-upP steps,0 covering0 0 the target DTMs, distributed initial transformations as T (0, 0, 0) for 3D points (O), and iteratively perform a consisting of both high-resolution regionsO that come from the original DLR HRSC level 4 Non-rigid Iterative Closest Point (NICP) fitting process. The final correspondences between DTMs and lower resolution (smoother) regions that were filled with up-sampled MOLA the target UCL HRSC level 4 DTMs (before co-alignment) and the reference DTM mosaic DTMs, together covering the target UCL HRSC level 4 DTMs. created at step (a) is recorded as T (dX, dY, dZ) for all 3D points (Q; Q=P+O). Note that the (b) Co-registration of the UCLQ HRSC level 4 ORIs with existing DLR HRSC level 4 final set of 3D correspondences TQ (dX, dY, dZ) is denser than the initial set of transforma- ORIs for overlapping regions and recording of the spatial correspondences for all 2 D tions TP (dX0, dY0, dZ0), as it has added in equally distributed initial “correspondences”, points (P), here denoted as TP (dX0, dY0), for the next step. The ORI co-registrations are i.e., TO, between HRSC and MOLA DTM, where there is no overlapping HRSC ORIs (see Figureperformed7 for anusing intuitive Mutual illustration), Shape Adapted but it Scale is still Invariant only a subset Feature (we Transform use between (MSA-SIFT), 1/50 and 1/100)which was of the introduced total HRSC in DTM[44] and 3D was points also due implemented to computational as part limitations. of the CASP-GO pipeline [39]. Note that the reason for using image co-registration prior to 3 D co-alignment is that images always have higher spatial resolution with more detailed textures comparing to any derived stereo-derived DTM, and thus result in more accurate spatial adjustments than simply performing a point-cloud alignment using Iterative Closest Point (ICP) based methods. Figure 6 shows that the 3 D co-alignment results for using point-cloud alignment only and using the proposed joint 3 D and image co-registration method. We can see that in both cases, the two HRSC DTMs are “co-aligned” at the DTM scale and could be “seamlessly” blended together. However, when looking at the ORIs, which are at a finer scale of resolution, the differences become much more obvious.

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(d) Non-rigid point cloud transformation is performed for the target UCL HRSC level 4 DTMs according to TQ (dX, dY, dZ) and a corresponding spatial transformation is performed for the target UCL HRSC level 4 ORIs according to TQ (dX, dY). Note that in most of the cases for this work, the target HRSC DTM/ORI have overlapping reference HRSC DTMs/ORIs orbital strips on either side. However, where more than one new orbital strip is needed to span a gap, we co-align these one at a time, first selecting at each step, the strip that has the greatest overlap with the data already in place. Then the corrected HRSC DTM/ORI is used as the reference for subsequent co-alignment of the target HRSC DTM/ORI that previously has smaller or no overlapping area. See Figure 7 for a schematic representation, i.e., in case 2, we start from Target-1 (T-1) and Target-3 (T3) HRSC DTM/ORI, then correct T-2 using the corrected T-1, T-3 and MOLA DTM as references.

(e) Finally, the differences between MOLA DTM and each corrected UCL HRSC level Figure 6. Side-by-sideFigure 6. Side-by-side view view ofcolourised of colourised4 DTMs asDTM well (top) (top)as andthe and ORIexisting (botto ORI m)DLR (bottom) of DLRHRSC HRSC of level DLR level 4 DTMs4 HRSC data (H2156_0000) are level checked 4 data and for (H2156_0000)the any overlapping remaining and the overlappingUCL UCL HRSC HRSC level 4 data level (H2134_0000). 4 datasystematic (H2134_0000). (a) and issues, (c): H2156_0000 e.g.,( jittera,c): DTMissues H2156_0000 with or “warppoint cloud DTMed edges”. alignmen with At point B-Splineapplied cloud using fitting the alignment algorithmASP’s “pc_align” appliedis used using to correct any remaining systematic issues. The B-Spline fitting algorithm uses the com- the ASP’sfunction, “pc_align” to the function,target DTM to(H2134_0000), the target and DTM the same (H2134_0000), transformation and is applied the same to the ORI. transformation (b) and (d): H2156_0000 is applied DTM to with the the ORI. (b,d): joint 3 D and image co-registrationputed as described planar B-Spline in the text, surface to the targetto represent DTM (H2134_0000), the HRSC and and MOLAthe same DTMs, transformation and then is apusesplied the H2156_0000to the DTM ORI.with the joint 3Dsame and NICP image method co-registration that is used as describedin step (c) and in the (d) text,to fit to the the two target B-Spline DTM surfaces, (H2134_0000), and and then applies the calculated non-rigid transformation to do a final correction of all HRSC the same transformation is applied to the(c) ORI.Take the spatial correspondences from (b), assuming an initial vertical transfor- DTMs (both DLR & UCL). mation equals to the absolute difference of height values for the corresponding 2 D points, P, here denoted as dZ0, as the initial transformation, i.e., TP (dX0, dY0, dZ0), adding equally distributed initial transformations as TO (0, 0, 0) for 3 D points (O), and iteratively perform a Non-rigid Iterative Closest Point (NICP) fitting process. The final correspondences between the target UCL HRSC level 4 DTMs (before co-alignment) and the reference DTM mosaic created at step (a) is recorded as TQ (dX, dY, dZ) for all 3 D points (Q; Q=P+O). Note that the final set of 3 D correspondences TQ (dX, dY, dZ) is denser than the initial set of transformations TP (dX0, dY0, dZ0), as it has added in equally distributed initial “correspondences”, i.e., TO, between HRSC and MOLA DTM, where there is no overlapping HRSC ORIs (see Figure 7 for an intuitive illustration), but it is still only a subset (we use between 1/50 and 1/100) of the total HRSC DTM 3 D points due to computational limitations.

Figure 7. Illustration of the relation between target HRSC DTM/ORI, and Reference HRSC DTM/ORI and MOLA DTM Figure 7. Illustration of the relation between target HRSC DTM/ORI, and Reference HRSC DTM/ORI and MOLA DTM within the within the joint 3D and image co-registration process. joint 3 D and image co-registration process. More specifically, the NICP algorithm used in step (c) and (d) is based on the Optimal Step NICP (OS-NICP) method [50] which is briefly described as follows. Firstly, assign one initial affine 3 × 4 transformation matrix and a weight term (w) to each 3 D correspondence in Q, to model the distance between the target and reference 3 D point clouds. Note that the weights are set to 0 for the target 3 D points where no corresponding reference 3 D points can be found. The weighted distance term and two additional regularisation terms together form the cost function of OS-NICP (sum of the three terms). The two regularisation terms are the stiffness term to penalise transformations of neighbouring 3

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(d) Non-rigid point cloud transformation is performed for the target UCL HRSC level 4 DTMs according to TQ (dX, dY, dZ) and a corresponding spatial transformation is performed for the target UCL HRSC level 4 ORIs according to TQ (dX, dY). Note that in most of the cases for this work, the target HRSC DTM/ORI have overlapping reference HRSC DTMs/ORIs orbital strips on either side. However, where more than one new orbital strip is needed to span a gap, we co-align these one at a time, first selecting at each step, the strip that has the greatest overlap with the data already in place. Then the corrected HRSC DTM/ORI is used as the reference for subsequent co-alignment of the target HRSC DTM/ORI that previously has smaller or no overlapping area. See Figure7 for a schematic representation, i.e., in case 2, we start from Target-1 (T-1) and Target-3 (T3) HRSC DTM/ORI, then correct T-2 using the corrected T-1, T-3 and MOLA DTM as references. (e) Finally, the differences between MOLA DTM and each corrected UCL HRSC level 4 DTMs as well as the existing DLR HRSC level 4 DTMs are checked for any remaining systematic issues, e.g., jitter issues or “warped edges”. A B-Spline fitting algorithm is used to correct any remaining systematic issues. The B-Spline fitting algorithm uses the computed planar B-Spline surface to represent the HRSC and MOLA DTMs, and then uses the same NICP method that is used in step (c) and (d) to fit the two B-Spline surfaces, and then applies the calculated non-rigid transformation to do a final correction of all HRSC DTMs (both DLR & UCL). More specifically, the NICP algorithm used in step (c) and (d) is based on the Optimal Step NICP (OS-NICP) method [50] which is briefly described as follows. Firstly, assign one initial affine 3 × 4 transformation matrix and a weight term (w) to each 3D correspondence in Q, to model the distance between the target and reference 3D point clouds. Note that the weights are set to 0 for the target 3D points where no corresponding reference 3D points can be found. The weighted distance term and two additional regularisation terms together form the cost function of OS-NICP (sum of the three terms). The two regularisation terms are the stiffness term to penalise transformations of neighbouring 3D points, and the landmark term to initialise and penalise the transformations of landmarks. The landmarks, in our case, are the 3D correspondences calculated via HRSC ORI-to-ORI co-registration, i.e., TP (dX0, dY0, dZ0). OS-NICP iteratively finds the optimal 3D correspondences TQ, while lowering the stiffness weights (to allow more localised transformations), until the cost function is minimised. Note that the correct alignment can also be found without landmarks in OS-NICP with a wider range of initial conditions. In the last step (e), a B-Spline fitting algorithm is used to remove any remaining systematic errors for both the UCL and DLR HRSC level 4 DTMs. The motivation for this step is based on our observation that some systematic errors still exist for both the DLR HRSC level 4 DTMs (after BA/SPA correction) and the UCL HRSC level 4 DTMs (after the joint 3D and image co-registration process). Considering two DTMs with the same resolution, size, and zero noise, if they are spatially co-registered, then the absolute height difference between the two DTMs would be the theoretical transformation to co-align each point of the two DTMs. However, we cannot use an absolute difference mapping to perform point-to-point corrections for HRSC DTM against MOLA DTM in our case, since that will remove all the details in the higher resolution HRSC DTM and make it identical to the MOLA DTM. Therefore, in this work, we propose using a B-Spline fitting algorithm to correct remaining systematic errors in both the DLR and UCL HRSC level 4 DTMs. The B-Spline fitting algorithm firstly computes two planar B-Spline surfaces, to represent the large-scale topography of HRSC and MOLA DTMs at a density of 2 km/pixel (~5 times the MOLA scale), then fit the two planar curves with NICP, and finally apply the calculated non-rigid transformation to the HRSC DTMs. In terms of computing the planar B-Spline curve representations, we used the asymmetric distance minimisation method described in [51] (implementation available through the Point Cloud Library’s Surface module at: http://pointclouds.org/documentation/tutorials/#surface (accessed on 21 March 2021)). Remote Sens. 2021, 13, 1385 14 of 26

A high-level workflow for fitting a B-Spline surface to the point-clouds has the following 4 steps: (1) initialise the B-Spline surface by calculating a bounding circle of the target point cloud and set the initial control points; (2) increase the number of control-points and subsequently fit the refined B-spline surface; (3) project the target point-cloud into the parametric domain using the closest points to the B-spline surface and cut off the overlapping regions of the surface; (4) repeat (2) and (3) until the desired density of control points is reached. Finally, the updated UCL and DLR HRSC level 4 DTMs are used to create a mosaic for the whole Valles Marineris area. We use the ASP “priority-blending” method to create the mosaic, in which case, DTMs are blended in the order of highest-to-lowest resolutions.

2.5. Automated Co-Registration and Orthorectiﬁcation Initially, the DLR HRSC level 4 ORIs (ND4 products) and the UCL HRSC level 4 ORIs, which were orthorectiﬁed and co-registered using HRSC level 2 nadir panchromatic images (ND2 products), were used to create the ORI mosaic of Valles Marineris. However, we found some of the UCL level 4 ORIs, that were produced from the ND2 products, have missing data in different areas of the strip (the missing data comes from the original raw HRSC data records). Also, some of the DLR level 4 ORIs (ND4 products) are narrower than Remote Sens. 2021, 13, x FOR PEER REVIEW 15 of 28 the level 2 stereo images (S12 and S22 products), leaving a seam gap between two adjacent ORIs (see Figure8).

Figure 8. Illustration of a combination of the DLR and new UCL HRSC level 4 ORIs (IDs are labelled in red), showing Figure 8. Illustration of a combination of the DLR and new UCL HRSC level 4 ORIs (IDs are labelled in red), showing completeness completeness before (a,c) and after (b,d) adding more additional overlapping UCL level 4 ORIs, produced from both the before (a & c) and after (b & d) adding more additional overlapping UCL level 4 ORIs, produced from both the nadir and stereo level nadir and stereo level 2 images (ND2 and S12/S22 products), to fill in the missing data and seam gaps. 2 images (ND2 and S12/S22 products), to fill in the missing data and seam gaps. Therefore,Therefore, additional additional HRSCHRSC level 2 stereo stereo images images (S12 (S12 and and S22 S22 products) products) are are used used to to covercover these these gap gap regions,regions, and further orthorecti orthorectificationfication and and co-registrations co-registrations are are performed. performed. Also,Also, due due to to missing missing data,data, gapsgaps appear at at different different locations locations in in the the different different HRSC HRSC level level 2 2 imagesimages (the (the level level 2 2 nadir nadir panchromatic panchromatic and and stereo stereo images, images, i.e., i.e., ND2, ND2, S12, S12, and and S22 S22 products) prod- foructs) a same for a strip. same Multiplestrip. Multiple HRSC HRSC level level 2 images 2 images are therefore are therefore sometimes sometimes required required in order in toorder create to thecreate final the Valles final MarinerisValles Marineris ORI mosaic ORI mosaic without without any gaps. any gaps. The additionalThe additional HRSC levelHRSC 2 images level 2 images (ND2, S12,(ND2, and S12, S22) and that S22) were that were needed needed to produce to produce overlapping overlapping new new level 4level ORIs 4 inORIs order in order to fill to in fill the in the gaps gaps of of the the original original 11 11 UCLUCL level 4 4 ORIs ORIs are are h2178_0000, h2178_0000, h2145_0000,h2145_0000, h2134_0000, h2134_0000, andand h2112_0000. The The additional additional HRSC HRSC level level 2 images 2 images (S12 (S12 and and S22)S22) that that were were needed needed toto produceproduce new “wider” “wider” level level 4 4ORIs ORIs in in order order to tofill fill the the thin thin seam seam gapsgaps of of the the original original DLRDLR levellevel 44 ORIsORIs areare h0931_0000 and and h0920_0000. h0920_0000. The The issues issues are are demonstrateddemonstrated in in Figure Figure8 for8 for the the original original HRSC HRSC level level 4 ORIs4 ORIs from from UCL UCL and and DLR DLR using using the nadirthe nadir panchromatic panchromatic images images only (ND4only (ND4 and ND2 and images)ND2 images) showing showing missing missing data anddata gaps, and in comparisongaps, in comparison to adding to in adding the additional in the additional overlapped overlapped UCL HRSC UCL level HRSC 4 ORIslevel (co-registered4 ORIs (co- andregistered orthorectified and orthorectified using ND2, usin S12,g andND2, S22 S12, images). and S22 images). In this work, we use an automated image co-registration and orthorectification workflow to produce multiple overlapping HRSC level 4 ORIs from both level 2 nadir panchromatic and stereo images (ND2, S12, S22 products). The implementation uses our inhouse MSA-SIFT tie-point based image co-registration pipeline [39,44] and the ASP’s image orthorectification workflow (the “mapproject” function) [43]. The processing follows the steps of map projection with MOLA, image co-registration with already processed and co-registered HRSC level 4 ORIs, and orthorectification with the HRSC DTM Valles Mar- ineris mosaic created at the previous stage. A flow diagram of the automated image co- registration and orthorectification processing chain is shown in Figure 9.

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In this work, we use an automated image co-registration and orthorectification workflow to produce multiple overlapping HRSC level 4 ORIs from both level 2 nadir panchromatic and stereo images (ND2, S12, S22 products). The implementation uses our inhouse MSA-SIFT tie-point based image co-registration pipeline [39,44] and the ASP’s image orthorectification workflow (the “mapproject” function) [43]. The processing follows the steps of map projection with MOLA, image co-registration with already processed and co-registered HRSC level 4 ORIs, and orthorectification with the HRSC DTM Valles Remote Sens. 2021, 13, x FOR PEER REVIEW 16 of 28 Marineris mosaic created at the previous stage. A flow diagram of the automated image co-registration and orthorectification processing chain is shown in Figure9.

Figure 9. Flow diagram of the automated image co-registrationco-registration and orthorectiﬁcationorthorectification processingprocessing chain.chain.

2.6. Brightness Correction and Mosaicing of ORIs A particular difficulty difﬁculty in compiling image mosaics for the surface of Mars arises from the varying varying optical optical properties properties of of the the atmosphe atmosphere.re. The The quantity quantity of ofdust dust carried carried by bythe theat- mosphereatmosphere changes changes as as a aconsequence consequence of of seasonal seasonal weather weather phenomena, affecting camera observations toto aa greatergreater oror smaller smaller degree degree over over different different parts parts of of the the planet. planet. This This is seenis seen in inindividual individual images images as as variations variations in in clarity clarit andy and contrast, contrast, resulting resulting from from how how much much light light is isscattered scattered both both onto onto the the ground—from ground – from directions directions other other than than the sourcethe source of illumination—as of illumination well– as well as into as into and and out out of theof the camera’s camera’s line line of of sight. sight. The The image image strips strips shown shown inin FigureFigure8 8 illustrates the problem. It It is difficult difﬁcult to make a physicalphysical correctioncorrection forfor thethe effect,effect, becausebecause the images images are are the the only only source source of ofinformation information on onthe theinstantaneous instantaneous atmospheric atmospheric dust dustcon- tent.content. A model-based A model-based measurement measurement of ofdust dust cont contentent from from an an image image requires requires some prior knowledge of how the surface should appear: on oncece more, the image is the only source for the information, making the problem underdetermined.underdetermined. We areare ableable toto approximate approximate a a solution solution by by making making use use of theof the fact fact that that large large scale scale albedo al- variationsbedo variations on Mars on areMars already are already known. known. The MGS’s The MGS’s TES was TES used was to used construct to construct a global a globalalbedo albedo map at map about at 7 about km/pixel 7 km/pixel [39]. By [39]. introducing By introducing the constraint the constraint that an that HRSC an mosaic HRSC mosaicshould conformshould conform to this albedo to this at albedo coarse resolution,at coarse resolution, we are able we to are bring able all to the bring images all intothe imagesmutual into brightness mutual consistency. brightness consistency. The main steps The ofmain the steps procedure of the are procedure as follows are [ 41as ]:follows (1)[41]: Lambert correction. The main effect of this step is to compensate the variation of 1) Lambertillumination correction. across The an image main effect caused of by this the step curvature is to compensate of the planet. the variation of illu- (2) minationGenerate across intermediate an image resolution caused by brightnessthe curvature reference of the planet. map. Images are divided 2) Generatecoarsely intointermediate cells (3 cells resolution across track; brightness a proportional reference number map. along-trackImages are according divided coarselyto the image). into cells The (3 image cells across is rescaled track; in a brightness proportional value number to match along-track the TES map according with a tocontinuous the image). interpolation The image isbetween rescaled cells.in brightness A mosaic value is created to match at moderate the TES map resolution with a continuous(400 m/pixel) interpolation with same between average cells. brightness A mosaic characteristics is created at as moderate the TES map. resolution Edge (400artefacts m/pixel) are diminishedwith same average using uniform brightness Gaussian characteristics blur. Theresulting as the TES mosaic map. has Edge about ar- tefacts3 km/pixel are diminished information using content, uniform but Gaussi with veryan blur. much The reduced resulting artefacts mosaic comparedhas about 3 tokm/pixel the TES information source, where content, the information but with very content much isreduced substantially artefacts lower compared than the to thenominal TES source, resolution. where the information content is substantially lower than the nominal resolution. 3) Generate full-resolution mosaic. The source images are brightness-referenced to the intermediate mosaic similarly to step (2), but this time using a smaller cell size (9×n). Overlapping images are feathered together over a 40-pixel range. 4) Image sequence. The overlapping sequence for the mosaic is optimised through visual inspection. The starting sequence places the shortest ground sampling distance at the top of the mosaic. Lower quality images are moved down in the sequence by estimating a longer effective ground sampling distance by comparison with neighbouring images.

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(3) Generate full-resolution mosaic. The source images are brightness-referenced to the intermediate mosaic similarly to step (2), but this time using a smaller cell size (9 × n). 5) ContrastOverlapping adjustment. images Images are feathered with higher together atmospheric over a 40-pixel scattering range. appear in the mosaic (4) asImage areas sequence.of low contrast. The overlapping The contrast sequence is increased for either the mosaic uniformly is optimised or with througha multi- pointvisual interpolated inspection. along-track The starting change. sequence places the shortest ground sampling distance 6) Iteration.at the top The of mosaic the mosaic. is regenerated Lower quality and st imageseps (4) areand moved (5) reconsidered down in the until sequence the result by estimating a longer effective ground sampling distance by comparison with neigh- is as close to visually homogeneous as possible. bouring images. 3.(5) Results Contrast adjustment. Images with higher atmospheric scattering appear in the mosaic as areas of low contrast. The contrast is increased either uniformly or with a multi- 3.1. HRSCpoint DTM interpolated Mosaic for along-track Valles Marineris change. (6) InIteration. this work, The we mosaic created is regenerated 11 high-quality and steps HRSC (4) level and (5)4 DTMs, reconsidered using a until hybrid the resultDTM processingis as close chain to that visually combine homogeneous matching resu as possible.lts from three different matchers, using the HRSC level 2 stereo images, to cover the gap regions from existing DLR HRSC level 4 DTMs3. Results for the Valles Marineris area. The resulting UCL level 4 DTMs are then simultane- ously3.1. HRSC co-aligned DTM Mosaicwith the for DLR Valles products Marineris and MOLA DTM, via a joint 3 D and image co- registrationIn this work,process, we to created allow seamless 11 high-quality mosaicing HRSC for levelthe whole 4 DTMs, of Valles using Marineris. a hybrid DTM The mosaicprocessing which chain just thatincludes combine the DLR matching DTM resultsproducts from is shown three differentin Figure matchers, 10. The final using result- the ingHRSC HRSC level DTM 2 stereo mosaic images, (50 tom/pixel), cover the using gap regionsthe 71 existing from existing DLR DTMs DLR HRSC and 11 level newly 4 DTMs cre- atedfor the DTMs Valles from Marineris UCL, is area. shown The in resulting Figure 11. UCL We level note 4that DTMs the arenew then HRSC simultaneously DTM mosaic co- is muchaligned more with complete the DLR covering products the and whole MOLA of DTM, Valles via Marineris a joint 3D area. and Some image cropped co-registration exam- plesprocess, are provided to allow seamless in Figure mosaicing 12 for zoom-in for the vi wholeews over of Valles different Marineris. terrain The types, mosaic including which hill/rockjust includes chaos, the craters, DLR DTMflat surface, products and is valley/cliffs, shown in Figure within 10 the. TheValles ﬁnal Marineris resulting area. HRSC DTMAlso, mosaic the (50final m/pixel), resulting using HRSC the DTM 71 existing mosaic has DLR shown DTMs improved and 11 newly co-alignment created DTMs accu- racyfrom against UCL, isthe shown MOLA in DTM, Figure which 11. Wecan notebe observed that the in new Figure HRSC 13 and DTM Figure mosaic 14, isin muchcom- parisonmore complete to the HRSC-to-MOLA covering the whole difference of Valles maps Marineris (at the scale area. of Some 200 m/pixel) cropped created examples before are andprovided after the in Figureproposed 12 for DTM zoom-in co-alignment views over method different (before terrain mosaicing). types, including hill/rock chaos, craters, ﬂat surface, and valley/cliffs, within the Valles Marineris area.

Figure 10. HRSC DTM mosaic created only with the existing DLR HRSC level 4 DTMs, showing incomplete coverage of FigureValles 10. HRSC Marineris. DTM mosaic created only with the existing DLR HRSC level 4 DTMs, showing incomplete coverage of Valles Marineris.

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FigureFigure 11. The 11. finalThe resulting ﬁnal resulting HRSCHRSC DTM mosaic DTM mosaic at 50 m/pixel, at 50 m/pixel, covering covering the whole the of whole Valles of Marineris. Valles Marineris. The four The areas four indicated areas are indicated are shown in greater detail in Figure 12. shownFigure in 11. greater The final detail resulting in Figure HRSC 12. DTM mosaic at 50 m/pixel, covering the whole of Valles Marineris. The four areas indicated are shown in greater detail in Figure 12.

FigureFigure 12. Examples 12. Examples of zoom-in of zoom-in views views using using colourised colourised by height by height and and hill-shaded hill-shaded DTMs DTMs over over different different features features within within the the Valles Valles Marineris DTM mosaic, including (a) hilly/rock chaos area; (b) crater area; (c) ﬂat surfaced area; (d) valley/cliff area. MarinerisFigure 12. DTM Examples mosaic, of includingzoom-in views (a) hilly/rock using colourised chaos area; by (b height) crater and area; hill-shaded (c) flat surfaced DTMs area; over ( ddifferent) valley/cliff features area. within the Valles Marineris DTM mosaic, including (a) hilly/rockAlso, the chaos ﬁnal resultingarea; (b) crater HRSC area; DTM (c) flat mosaic surfaced has area; shown (d) improvedvalley/cliff co-alignmentarea. accuracy against the MOLA DTM, which can be observed in Figures 13 and 14, in comparison

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As shown in the difference maps comparing the single-strip HRSC level 4 DTMs, before (refer to Figure 13) and after (refer to Figure 14) our proposed joint 3 D and image co-registration, against MOLA DTM, not only removes the systematic errors of the single- strip HRSC level 4 DTMs, e.g. jitter [38] and “tilted ends” (refer to Figure 13 for an example of these issues with jitter shown in the blue-to-red-to-blue differences and “tilted ends” where the end of a strip shows blue differences), have been corrected, but also local misalignments have been significantly reduced. Even though some local disagreement still exists in a few areas, most of the area, i.e., ~80.4% of all DTMs, has less than 30 m difference comparing to MOLA and about half, i.e., ~55.7% of all DTMs, has less than 15 m difference comparing to MOLA. On the other hand, without 3 D co-alignment, only ~60.3% of the Remote Sens. 2021, 13, 1385 HRSC DTM pixels has less than 30 m difference to MOLA, and ~49.1% of the HRSC18 DTM of 26 pixels has less than 15 m difference to MOLA. This suggests the vertical accuracy, after the joint 3 D and image co-registration, has had a significant positive improvement for largeto the misalignments HRSC-to-MOLA (>30 difference m), and some maps fair (at theimprovement scale of 200 for m/pixel) small misalignments created before (<30 and m).after the proposed DTM co-alignment method (before mosaicing).

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FigureFigure 13. 13. HRSC-to-MOLAHRSC-to-MOLA difference difference map map before 3 3D D co-alignment.co-alignment.

Figure 14. HRSC-to-MOLA difference map after 3D co-alignment. Figure 14. HRSC-to-MOLA difference map after 3 D co-alignment. As shown in the difference maps comparing the single-strip HRSC level 4 DTMs, As for the mosaicing process, we used the higher resolution HRSC level 4 DTMs (57 before (refer to Figure 13) and after (refer to Figure 14) our proposed joint 3D and image from DLR and 11 from UCL at 50 m/pixel) in front and the lower resolution ones on the co-registration, against MOLA DTM, not only removes the systematic errors of the single- back (6 from DLR at 100 m/pixel, then 8 from DLR at 150 m/pixel). This means that alt- strip HRSC level 4 DTMs, e.g. jitter [38] and “tilted ends” (refer to Figure 13 for an example hough the Valles Marineris DTM mosaic is gridded at 50 m/pixel, some regions are mosaiced with upsampled 100 m/pixel or 150 m/pixel DTMs. Therefore, for those areas, they will show less detail than the areas that were mosaiced with native 50 m/pixel DTMs. Here we provide a colourised footprint figure, see Figure 15, to distinguish the native mapping resolutions of the Valles Marineris DTM mosaic. The lower DTM resolution is due to the lower resolution input stereo images, i.e., 12.5 m/pixel images produce 50 m/pixel DTMs, 25 m/pixel images produce 100 m/pixel DTMs, 50 m/pixel images produce 150 m/pixel DTMs, so Figure 15 also indicates the high-resolution and low-resolution distributions of the Valles Marineris ORI mosaic (see Section 3.2).

Figure 15. Footprint map showing different native resolution (Green for 50 m/pixel, Red for 100 m/pixel, and Blue for 150 m/pixel) distributions for different regions within the 50 m/pixel Valles Marineris HRSC DTM mosaic.

Examples of HRSC-to-MOLA profile locations are shown in Figure 16 and the results are shown in Figure 17 for Profile A, Figure 18 for Profile B, Figure 19 for Profile C, Figure

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of these issues with jitter shown in the blue-to-red-to-blue differences and “tilted ends” where the end of a strip shows blue differences), have been corrected, but also local misalignments have been significantly reduced. Even though some local disagreement still exists in a few areas, most of the area, i.e., ~80.4% of all DTMs, has less than 30 m difference comparing to MOLA and about half, i.e., ~55.7% of all DTMs, has less than 15 m difference comparing to MOLA. On the other hand, without 3D co-alignment, only ~60.3% of the HRSC DTM pixels has less than 30 m difference to MOLA, and ~49.1% of the HRSC DTM pixels has less than 15 m difference to MOLA. This suggests the vertical accuracy, after the Figurejoint 14. 3D HRSC-to-MOLA and image co-registration, difference map has after had 3 D aco-alignment. significant positive improvement for large misalignments (>30 m), and some fair improvement for small misalignments (<30 m). AsAs for for the the mosaicing mosaicing process, process, we used we used the higher the higher resolution resolution HRSC HRSC level level4 DTMs 4 DTMs (57 from(57 DLR from and DLR 11 and from 11 UCL from at UCL 50 m/pixel) at 50 m/pixel) in front in and front the and lower the lowerresolution resolution ones on ones the on backthe (6 back from (6 DLR from at DLR 100 at m/pixel, 100 m/pixel, then 8 then from 8 DLR from at DLR 150 at m/pixel). 150 m/pixel). This means This means that alt- that houghalthough the Valles the Valles Marineris Marineris DTM DTMmosaic mosaic is gridded is gridded at 50 m/pixel, at 50 m/pixel, some regions some regions are mo- are saicedmosaiced with upsampled with upsampled 100 m/pixel 100 m/pixel or 150 orm/pi 150xel m/pixel DTMs. DTMs.Therefore, Therefore, for those for areas, those they areas, willthey show will less show detail less than detail the than areas the that areas were that mosaiced were mosaiced with native with 50 native m/pixel 50 m/pixelDTMs. Here DTMs. weHere provide we providea colourised a colourised footprint footprintfigure, see figure, Figure see 15, Figureto distinguish 15, to distinguish the native mapping the native resolutionsmapping of resolutions the Valles ofMarineris the Valles DTM Marineris mosaic.DTM The lower mosaic. DTM The resolution lower DTM is due resolution to the loweris due resolution to the lowerinput stereo resolution images, input i.e., stereo 12.5 m/pixel images, images i.e., 12.5 produce m/pixel 50 m/pixel images DTMs, produce 25 50m/pixel m/pixel images DTMs, produce 25 m/pixel 100 m/pixel images DT produceMs, 50 100m/pixel m/pixel images DTMs, produce 50 m/pixel 150 m/pixel images DTMs,produce so Figure 150 m/pixel 15 also indicates DTMs, so the Figure high-res 15 olutionalso indicates and low-resolution the high-resolution distributions and low-of theresolution Valles Marineris distributions ORI mosaic of the Valles(see Section Marineris 3.2). ORI mosaic (see Section 3.2).

FigureFigure 15. 15.FootprintFootprint map mapshowing showing different different native native resolution resolution (Green (Greenfor 50 m/pixel, for 50m/pixel, Red for 100 Red for m/pixel,100 m/pixel, and Blue and for Blue150 m/pixel) for 150m/pixel) distributions distributions for different for regions different within regions the within 50 m/pixel the 50 Valles m/pixel MarinerisValles Marineris HRSC DTM HRSC mosaic. DTM mosaic.

ExamplesExamples of ofHRSC-to-MOLA HRSC-to-MOLA profile profile locations locations are are shown shown in inFigure Figure 16 16 and and the the results results areare shown shown in inFigure Figure 17 17 for for Profile Profile A, A, Figure Figure 18 18 for for Profile Profile B, B, Figure Figure 1919 for ProfileProfile C,C, FigureFigure 20 for Profile D, and Figure 21 for Profile E. Profile A covers a broad extent, crossing multiple DLR HRSC level 4 DTMs and UCL HRSC level 4 DTMs, and has shown smooth transitions between UCL and DLR products for the whole mosaic product. Profile B is a shorter line that contains only one UCL products in between two DLR products, a smooth transition from UCL to DLR products can be clearly observed at both sides. Also, Profile B shows no systematic cross-track offset at both ends of the UCL product. Profile C is a long vertical line crossing only one UCL product and shows no systematic along-track offset over a larger extent. On the other hand, Profile D is a shorter vertical line crossing the edge of the same UCL product shown in Profile C. Profile D shows the details of the HRSC DTM compared to MOLA DTM as well as good alignment between the two. Also, Profile D has shown that, after our 3D co-alignment process, there is no along-track warping issue near the DTM edge, which has been a common issue for stereo-derived DTMs. Finally, Profile E shows a short horizontal line, crossing both sides of the border of the UCL product. Profile E also shows the details of the good alignment of the HRSC DTM against the MOLA DTM. Also, Profile E shows that there are no across-track warping errors at either side of the DTM edges. Remote Sens. 2021, 13, x FOR PEER REVIEW 21 of 28

Remote Sens. 2021, 13, x FOR PEER REVIEW 21 of 28 20 for Profile D, and Figure 21 for Profile E. Profile A covers a broad extent, crossing multiple DLR HRSC level 4 DTMs and UCL HRSC level 4 DTMs, and has shown smooth transitions between UCL and DLR products for the whole mosaic product. Profile B is a shorter20 for Profile line that D, containsand Figure only 21 forone Profile UCL prE. oductsProfile inA coversbetween a broad two DLR extent, products, crossing a mul- smooth transitiontiple DLR from HRSC UCL level to 4DLR DTMs products and UCL can HRSC be clearly level observed4 DTMs, andat both has sides.shown Also, smooth Profile Btransitions shows no between systematic UCL cross-track and DLR productsoffset at forbo ththe ends whole of mosaicthe UCL product. product. Profile Profile B is C a is a longshorter vertical line that line contains crossing only only one one UCL UCL products product in between and shows two DLR no systematic products, a along-tracksmooth transition from UCL to DLR products can be clearly observed at both sides. Also, Profile offset over a larger extent. On the other hand, Profile D is a shorter vertical line crossing B shows no systematic cross-track offset at both ends of the UCL product. Profile C is a the edge of the same UCL product shown in Profile C. Profile D shows the details of the long vertical line crossing only one UCL product and shows no systematic along-track HRSC DTM compared to MOLA DTM as well as good alignment between the two. Also, offset over a larger extent. On the other hand, Profile D is a shorter vertical line crossing Profilethe edge D hasof the shown same that, UCL after product our 3shown D co-alignment in Profile C. process, Profile Dthere shows is no the along-track details of the warp- ingHRSC issue DTM near compared the DTM to edge, MOLA which DTM has as wellbeen as a goodcommon alignment issue betweenfor stereo-derived the two. Also, DTMs. Finally,Profile DProfile has shown E shows that, aafter short our horizontal 3 D co-alignment line, crossing process, boththere sidesis no along-track of the border warp- of the UCL product. Profile E also shows the details of the good alignment of the HRSC DTM Remote Sens. 2021, 13, 1385 ing issue near the DTM edge, which has been a common issue for stereo-derived DTMs. 20 of 26 againstFinally, the Profile MOLA E shows DTM. a shortAlso, horizontalProfile E showsline, crossing that there both aresides no of across-track the border of warping the errorsUCL product.at either Profileside of E the also DTM shows edges. the details of the good alignment of the HRSC DTM against the MOLA DTM. Also, Profile E shows that there are no across-track warping errors at either side of the DTM edges.

FigureFigure 16. 16.HRSC HRSC DTM DTM mosaic mosaic showing showing locations of of the the 5 5profile proﬁle lines lines (A,(A, B, C, B, D, C, E). D, E). Figure 16. HRSC DTM mosaic showing locations of the 5 profile lines (A, B, C, D, E).

Figure 17. Height comparison between HRSC DTM (Red) and MOLA DTM (Blue) for Proﬁle A. The DLR HRSC level 4 FigureRemoteFigure 17. Sens. 17. Height Height2021, 13comparison ,comparison x FOR PEER between betweenREVIEW HRSC HRSC DTMDTM (Red) andand MOLAMOLA DT DTMM (Blue) (Blue) for for Profile Profile A. A.The The DLR DLR HRSC HRSC level level 4 DTMs 4 22DTMs ofand 28 and Remote Sens. 2021, 13, x FOR PEER REVIEW 22 of 28 DTMsUCL UCL HRSC and HRSC UCL level level HRSC4 DTMs4 DTMs level are are labelled 4labelled DTMs in in are Yellow Yellow labelled and Green in Yellow colour,colour, and respectively. respectively. Green colour, respectively.

FigureFigure 18. 18Height. Height comparison comparison between between HRSC HRSC DTM DTM (Red)(Red) and MOLA and MOLADTM (Blue) DTM for (Blue)Profile B. for The Proﬁle DLR HRSC B. The level DLR 4 DTMs HRSC and level 4 DTMsFigureUCL and HRSC 18 UCL. Height level HRSC 4 comparison DTMs level are 4labelled between DTMs as are HRSC Yellow labelled DTM and (Red)Green as Yellow and colour, MOLA and respectively. Green DTM (Blue) colour, for respectively.Profile B. The DLR HRSC level 4 DTMs and UCL HRSC level 4 DTMs are labelled as Yellow and Green colour, respectively.

Figure 19. Height comparison between HRSC DTM (Red) and MOLA DTM (Blue) for Profile C. This profile line only contains the Figure 19. Height comparison between HRSC DTM (Red) and MOLA DTM (Blue) for Proﬁle C. This proﬁle line only FigureUCL HRSC 19. Height level 4 comparison DTM. between HRSC DTM (Red) and MOLA DTM (Blue) for Profile C. This profile line only contains the containsUCL HRSC the UCL level HRSC 4 DTM. level 4 DTM.

Figure 20. Height comparison between HRSC DTM (Red) and MOLA DTM (Blue) for Profile D. This profile line only contains the FigureUCL HRSC 20. Height level 4 comparison DTM. between HRSC DTM (Red) and MOLA DTM (Blue) for Profile D. This profile line only contains the UCL HRSC level 4 DTM.

Figure 21. Height comparison between HRSC DTM (Red) and MOLA DTM (Blue) for Profile E. This profile line only contains the FigureUCL HRSC 21. Height level 4 comparison DTM. between HRSC DTM (Red) and MOLA DTM (Blue) for Profile E. This profile line only contains the UCL HRSC level 4 DTM. 3.2. HRSC ORI Mosaic for Valles Marineris 3.2. HRSC ORI Mosaic for Valles Marineris The mosaic from the orthoimages after Lambertian correction, brightness referencing, manualThe mosaic contrast from adjustment the orthoimages and image after sequ Lambencingertian is correction, shown in Figure brightness 22. In referenc- general, ing,the transitionsmanual contrast from oneadjustment image to and another image are sequ seamless,encing isalthough shown in in Figure a few cases22. In itgeneral, is pos- thesible transitions to see some from change one inimage image to quality.another It are should seamless, be noted although that HRSC in a few observations, cases it is pos- due sibleto its toelliptical see some orbit change has equatorin image crossing quality. overIt shouldpasses be at noted different that HRSCtimes ofobservations, day, so that due the tosolar its illuminationelliptical orbit is hasnot equatorusually consistentcrossing over betweenpasses one at different strip and times the next. of day, There so thatare also the solar illumination is not usually consistent between one strip and the next. There are also

Remote Sens. 2021, 13, x FOR PEER REVIEW 22 of 28 Remote Sens. 2021, 13, x FOR PEER REVIEW 22 of 28

Figure 18. Height comparison between HRSC DTM (Red) and MOLA DTM (Blue) for Profile B. The DLR HRSC level 4 DTMs and Figure 18. Height comparison between HRSC DTM (Red) and MOLA DTM (Blue) for Profile B. The DLR HRSC level 4 DTMs and UCL HRSC level 4 DTMs are labelled as Yellow and Green colour, respectively. UCL HRSC level 4 DTMs are labelled as Yellow and Green colour, respectively.

Remote Sens. 2021, 13, 1385 21 of 26 Figure 19. Height comparison between HRSC DTM (Red) and MOLA DTM (Blue) for Profile C. This profile line only contains the Figure 19. Height comparison between HRSC DTM (Red) and MOLA DTM (Blue) for Profile C. This profile line only contains the UCL HRSC level 4 DTM. UCL HRSC level 4 DTM.

FigureFigure 20. 20.Height Height comparison comparison between between HRSC HRSC DTM DTM (Red) (Red)and MOLA and DTM MOLA (Blue) DTM for Profile (Blue) D. for This Proﬁle profile D. line This only proﬁle contains line the only Figure 20. Height comparison between HRSC DTM (Red) and MOLA DTM (Blue) for Profile D. This profile line only contains the containsUCL HRSC the UCL level HRSC 4 DTM. level 4 DTM. UCL HRSC level 4 DTM.

Figure 21. Height comparison between HRSC DTM (Red) and MOLA DTM (Blue) for Profile E. This profile line only contains the FigureFigure 21. 21.Height Height comparisoncomparison between between HRSC HRSC DTM DTM (Red) (Red)and MOLA and DTM MOLA (Blue) DTM for Profile (Blue) E. for This Proﬁle profile E.line This onlyproﬁle contains line the only UCL HRSC level 4 DTM. containsUCL HRSC the UCL level HRSC 4 DTM. level 4 DTM. 3.2. HRSC ORI Mosaic for Valles Marineris 3.2. HRSC ORI Mosaic for Valles Marineris 3.2. HRSCThe ORI mosaic Mosaic from forthe Valles orthoimages Marineris after Lambertian correction, brightness referencing, manualThe mosaic contrast from adjustment the orthoimages and image after sequ Lambencingertian is correction, shown in Figure brightness 22. In referenc- general, ing,The manual mosaic contrast from adjustment the orthoimages and image after sequ Lambertianencing is shown correction, in Figure brightness 22. In general, referencing, manualthe transitions contrast from adjustment one image and to another image sequencingare seamless, isalthough shown in in a few Figure cases 22 it. is In pos- general, the thesible transitions to see some from change one inimage image to quality.another Itare should seamless, be noted although that HRSC in a few observations, cases it is pos- due transitionssible to see from some one change image in image to another quality. are It should seamless, be noted although that HRSC in a observations, few cases it due is possible to Remote Sens. 2021, 13, x FOR PEER REVIEWto its elliptical orbit has equator crossing overpasses at different times of day, so that the 23 of 28

seeto somesolar its ellipticalillumination change orbit in is image has not equatorusually quality. crossingconsistent It should over betweenpasses be notedone at different strip that and HRSCtimes the next. of observations, day, There so thatare alsothe due to its ellipticalsolar illumination orbit hasequator is not usually crossing consistent overpasses between atone different strip and timesthe next. of There day, soare thatalso the solar illumination is not usually consistent between one strip and the next. There are also a a couple of images which show morning fog on the floor of the valley for which there is couple of images which show morning fog on the ﬂoor of the valley for which there is still still no alternative coverage: the transition from fog to no fog remains visible. The product no alternative coverage: the transition from fog to no fog remains visible. The product was was constructed at 12.5 m/pixel in 16-bit greyscale, and totals 120 GB. constructed at 12.5 m/pixel in 16-bit greyscale, and totals 120 GB.

FigureFigure 22. 22.HRSC HRSC 12.5 12.5 m/pixelm/pixel image image mosaic mosaic of of Valles Valles Marineris. Marineris.

3.3. Access to the Products The resultant HRSC DTM and ORI mosaic for the Valles Marineris area will be viewable through an interactive webGIS system (http://www.i-mars.eu/web-gis) developed at the Free University Berlin [52]. The mosaic products are also being made publicly available through the ESA Guest Storage Facility (GSF) via their DOI [53] (see list of DOIs at https://www.cosmos.esa.int/web/psa/ucl-mssl_meta-gsf). A link will also be established between the webGIS and the source-file and vice versa to ease visualisation and access/downloading.

Remote Sens. 2021, 13, 1385 22 of 26

3.3. Access to the Products The resultant HRSC DTM and ORI mosaic for the Valles Marineris area will be viewable through an interactive webGIS system (http://www.i-mars.eu/web-gis (accessed on 21 March 2021)) developed at the Free University Berlin [52]. The mosaic products are also being made publicly available through the ESA Guest Storage Facility (GSF) via their DOI [53] (see list of DOIs at https://www.cosmos.esa.int/web/psa/ucl-mssl_meta-gsf (accessed on 21 March 2021)). A link will also be established between the webGIS and the source-ﬁle and vice versa to ease visualisation and access/downloading.

4. Discussion This paper presented our 3D mapping and mosaicing work of Valles Marineris using HRSC level 2 stereo images and pre-existing level 4 products. We highlighted a collection of methods, in order to produce the resultant HRSC DTM and ORI mosaic, which include a new hybrid HRSC DTM processing chain for single-strip DTM production, a joint 3 D and image co-registration system to co-align all single-strip products with each other and simultaneously co-align with MOLA DTM, and finally an ORI co-registration and mosaicing method to create the ORI mosaic. The proposed hybrid stereo matching method may not be practical for general stereo mapping tasks due to its high computational load. However, it is capable of achieving better quality for small-to-medium sized 3D reconstruction jobs comparing to the experimental single-matcher driven methods. The hybrid method can also be used to combine other unique matchers as far as they provide unique advantages over different circumstances of stereo matching. Similarly, if a stereo matching algorithm is highly dependent on the set parameters, then this hybrid method can also be applied to improve the stereo result by merging different disparity maps produced with different parameters of the same matcher. In future experiments, we plan to explore the advantages and disadvantages of more global matchers and, as well as deep-learning based methods, that have been proven successful in the field of stereo vision (https://vision.middlebury.edu/stereo (accessed on 21 March 2021)) and try different combinations for more robust and more accurate matching of planetary images. The disparity blending scheme proposed at the disparity merging step, can also be improved in future studies, as currently it only uses a small number (nearest 8 neighbours) of spatial information of the disparities. For example, if the disparity blending scheme considers a larger group of relevant neighbouring disparity values using the idea of co-kriging [54], the blended disparity might have better robustness towards matching artefacts and gaps. Note that the HRSC stereo images do not normally have significant brightness/contrast /clarity/shadow differences between different off-nadir views as the HRSC instrument ac- quires continuous along-track acquisitions in a single orbit for stereo images (minimal time interval of up to ~30 seconds). However, for across-track repeat images such as CTX and HiRISE stereo, there can be substantial differences in image brightness/contrast/shadow, and the aforementioned clarity differences caused by different native resolution and noise level is a common challenge for HiRISE stereo. The proposed HRSC hybrid DTM processing chain can be further applied to produce robust results over CTX and HiRISE stereo images. As for the proposed joint 3D and image co-registration method, one of the key advantages is its high spatial and vertical alignment accuracy to the reference data, as we have demonstrated in the difference map shown in Figure 14 and follow-up profile analysis. The joint 3D and image co-registration method makes use of the finer-scale image-to-image correspondences as initial and “landmark” correspondences in a coarser-scale 3D-to-3D matching scenario. However, the main limitation for this method is that it always requires a reference data. Where there is more than one sequential co-alignment steps needed, as described in Section 2.4 (d), there may be an accumulated HRSC-to-HRSC error in the centre, even though the HRSC-to-MOLA error is minimised. Also, the BA/SPA procedure updates the orientation data to correct image and DTM distortions. Therefore, it can be Remote Sens. 2021, 13, 1385 23 of 26

advantageous to perform such relative adjustment as a first step before any further im- provements on 3D co-alignment, and even essential if there is a large distortion in any of the input HRSC level 2 stereo images. No such incidences were observed here. In this work, we assume the existing DLR HRSC level 4 v50+ products (initially) and MOLA DTM are trustable reference data. Note that our proposed joint 3D and image co-registration method is not meant as a replacement of the BA/SPA procedure for initial (and batch) HRSC corrections. Instead, it is meant to act as an alternative method for new target HRSC corrections, where there are sufficient overlapping reference products, and also a complementary method to improve the HRSC-to-MOLA co-alignment after BA/SPA. All HRSC single-strip DTMs (both UCL and DLR) are finally corrected against MOLA DTM, assuming there are no significant artefacts or topographical changes at the MOLA scale. Regarding the final HRSC DTM mosaic, overall quality has been visually checked and co-alignment accuracy has been quantified. However, the dataset is very large, and the stereo images are limited, so some local artefacts cannot be fully removed. There are known artefacts, including: tiling artefacts (seam lines), small interpolated-regions, smoothed area that were caused by using “foggy” HRSC stereo images (no other options were available as we have checked), occasional artefacts inside small craters, quilting artefacts at heavily shaded cliff areas, and the junction of lower resolution with higher resolution single-strip Remote Sens. 2021, 13, x FOR PEER REVIEWDTMs. These artefacts are illustrated in Figure 23. Methods have been developed25 for of 28 their

removal, where feasible but were beyond the scope of this paper.

Figure 23. Examples of “known artefacts” that appeared in the Valles Marineris HRSC DTM mosaic. (a) large scale tiling Figure 23. Examples of “known artefacts” that appeared in the Valles Marineris HRSC DTM mosaic. (a) large scale tiling artefact artefact (seam lines); (b) ﬁner scale tiling artefact (seam lines) and small interpolated areas where matching has failed; (seam lines); (b) finer scale tiling artefact (seam lines) and small interpolated areas where matching has failed; (c) large “smoothed (c) large “smoothed out” region inside the valley caused by using foggy input images that are not replaceable; (d) some out” region inside the valley caused by using foggy input images that are not replaceable; (d) some minor artefact in very small minor artefact in very small craters; (e) quilting artefact at heavily shaded cliff areas; (f) the effect of joining higher resolution craters; (e) quilting artefact at heavily shaded cliff areas; (f) the effect of joining higher resolution (50 m/pixel; from the left side) and (50 m/pixel; from the left side) and lower resolution (150 m/pixel; from the right side) single-strip DTMs. lower resolution (150 m/pixel; from the right side) single-strip DTMs. 5. Conclusions 5. Conclusions In this paper, we have introduced a complete processing system to produce HRSC level 4In DTMs this paper, over anwe extensive have introduced area, co-alignment a complete withprocessing existing system HRSC tolevel produce 4 and HRSC MOLA level 4 DTMs over an extensive area, co-alignment with existing HRSC level 4 and MOLA DTMs, and the creation of a DTM and ORI mosaic that covers the large canyon system of DTMs, and the creation of a DTM and ORI mosaic that covers the large canyon system of Valles Marineris. We used a hybrid DTM processing chain that combines the matching Valles Marineris. We used a hybrid DTM processing chain that combines the matching result from three different matchers to produce high-quality HRSC single-strip DTMs. result from three different matchers to produce high-quality HRSC single-strip DTMs. Then we introduced our joint 3 D and image co-registration processing chain for high- accuracy HRSC-to-HRSC and HRSC-to-MOLA co-alignment and DTM mosaicing. Fi- nally, automated image co-registration and orthorectification, and brightness correction methods are introduced for the production of Valles Marineris ORI mosaic. As the result, we generated 11 new HRSC level 4 single-strip DTMs and ORIs that have filled the gap regions from existing DLR HRSC level 4 products. We then co-aligned the new HRSC DTM/ORIs with existing DLR HRSC DTM/ORIs, and for both of the UCL and DLR products, co-aligned with MOLA. The Valles Marineris DTM mosaic is therefore created using 82 co-aligned single-strip DTMs and the Valles Marineris ORI mosaic is subsequently created using 94 co-registered, brightness and contrast corrected single-strip ORIs. HRSC-to-MOLA difference map and profile analysis are demonstrated, examples are shown for evaluation of the mosaic product, and known artefacts are summarised and demonstrated. The Valles Marineris mosaic product will be publicly available through the iMars webGIS and ESA GSF system. In the near future, we will look at applying the shape-from- shading method such as [55] to further improve quality and reduce artefact in the DTMs. In the future, we also plan to also build a HRSC colour ORI mosaic, and as well as the CTX DTM/ORI mosaic by co-aligning the CTX products we have processed for Valles Marineris, some of which have already been released through

Remote Sens. 2021, 13, 1385 24 of 26

Then we introduced our joint 3D and image co-registration processing chain for high- accuracy HRSC-to-HRSC and HRSC-to-MOLA co-alignment and DTM mosaicing. Finally, automated image co-registration and orthorectification, and brightness correction methods are introduced for the production of Valles Marineris ORI mosaic. As the result, we generated 11 new HRSC level 4 single-strip DTMs and ORIs that have filled the gap regions from existing DLR HRSC level 4 products. We then co-aligned the new HRSC DTM/ORIs with existing DLR HRSC DTM/ORIs, and for both of the UCL and DLR products, co-aligned with MOLA. The Valles Marineris DTM mosaic is therefore created using 82 co-aligned single-strip DTMs and the Valles Marineris ORI mosaic is subsequently created using 94 co-registered, brightness and contrast corrected single-strip ORIs. HRSC-to-MOLA difference map and profile analysis are demonstrated, examples are shown for evaluation of the mosaic product, and known artefacts are summarised and demonstrated. The Valles Marineris mosaic product will be publicly available through the iMars webGIS and ESA GSF system. In the near future, we will look at applying the shape-from- shading method such as [55] to further improve quality and reduce artefact in the DTMs. In the future, we also plan to also build a HRSC colour ORI mosaic, and as well as the CTX DTM/ORI mosaic by co-aligning the CTX products we have processed for Valles Marineris, some of which have already been released through (https://www.cosmos.esa.int/web/ psa/UCL-MSSL_iMars_CTX_v1.0 (accessed on 21 March 2021)).

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/rs13071385/s1. All figures in full-resolution are available as supplementary material. A list of the HRSC IDs used in this work is also provided. The HRSC and CTX mosaic products are available online. Author Contributions: Conceptualization, Y.T., J.-P.M., S.J.C.; methodology, Y.T., J.-P.M., G.M.; software, Y.T., G.M.; validation, Y.T., J.-P.M., S.J.C.; formal analysis, Y.T., J.-P.M.; investigation, J.-P.M., S.J.C., Y.T.; data curation, Y.T., A.R.D.P., S.J.C., J.-P.M.; writing—original draft preparation, Y.T., G.M., J.-P.M.; writing—review and editing, J.-P.M., Y.T., S.J.C.; visualization, Y.T. and A.R.D.P.; supervision, J.-P.M., Y.T., S.J.C.; project administration, J.-P.M., Y.T.; funding acquisition, J.-P.M., Y.T., S.J.C. All authors have read and agreed to the published version of the manuscript. Funding: The research leading to these results is receiving funding from the UKSA Aurora programme (2018–2021) under grant no. ST/S001891/1. Partial funding has been received from the STFC “MSSL Consolidated Grant” ST/K000977/1. SJC is thankful to the CNES for supporting her HRSC-related work. Data Availability Statement: The data products are all available via the DOI being added to the summary page for UCL products at https://www.cosmos.esa.int/web/psa/ucl-mssl_meta-gsf (accessed on 21 March 2021). Acknowledgments: The research leading to these results is receiving funding from the UKSA Aurora programme (2018–2021) under grant no. ST/S001891/1. Partial funding has been received from the STFC “MSSL Consolidated Grant” ST/K000977/1. SJC is thankful to the CNES for supporting her HRSC-related work. Conflicts of Interest: The authors declare no conflict of interest.

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