UCL 3D IMAGING PRODUCTS FROM HRSC FOR SPRC AND GLOBAL CTX, THEIR QUALITY ASSESSMENT AND THEIR PUBLIC DISTRIBUTION. J-. P. Muller1, A.R.D. Putri1 , Y. Tao1, G. Watson1, S. Besse2, E. Grotheer2, 1Imaging Group, Mullard Space Science Laboratory, Department of Space & Climate Physics, University College London, Holmbury St Mary, Surrey, RH5 6NT, j.muller@ucl.ac.uk, 2European Space Astronomy Centre (ESAC), Camino Bajo del Castillo, Ur. Villafranca del Castillo, 28692 Villanueva de la Canada, Madrid, Spain, sbesse@sciops.esa.int

Introduction: The EU-FP7 iMars project

(http://www.i-mars.eu) developed a completely auto-mated digital photogrammetric processing system, called CASP-GO [1] for batch processing of thousands of serendipitous stereo acquisitions from the NASA Context Camera and HiRISE instrument [2]. iMars also developed a web-GIS system for display of browse col-ourised by height hill-shaded products and the down-loading of original DTM (Digital Terrain Model) and ORI (OrthoRectified Image) [3]. Previously, UCL [4] developed a modification of the NASA-VICAR pro-cessing system for HRSC [5] which is called the KM processing system. This has recently been employed to generate orbital strip and mosaiced products of the Mar-tian SPRC (South Polar Residual Cap) [8]. We report on how all of these products are being quality assessed, classified and distributed.

Products: The CASP-GO processing chain was ap-plied to generate ~5,300 NASA Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) stereo-derived 3D imaging products using the MSSL-Imaging pro-cessing cluster and the Microsoft Azure® cloud compu-ting platform [1]. These DTMs cover ~18% of the Mar-tian surface at 18m/pixel compared to the current HRSC DTM coverage of around 50% with grid-spacing from 50m/pixel to 150m/pixel. Figure 1 shows their overall distribution when compared against previously pro-cessed HRSC level-4 products [5].

Figure 1. Footprints of the processed iMars CTX blue for HRSC products available and (red for missing HRSC prod-ucts) and HiRISE (green) DTMs displayed on top of the col-ourised_by_height hill-shaded HRSC level 4 DTM products and greyscale hill-shaded MOLA DTM.

Quality assessment: Before these products can be released, a 3-tiered quality assessment process was un-dertaken. In the first, a sample of ≈1,000 DTM products

were visually assessed by 3 volunteers and placed into 5 different quality categories. These quality classifica-tions were then compared against each other and are shown in Figure 2 and Table 1 below. Note the good agreement for the poorest and best quality and the vari-able agreement for other categories.

Figure 2. Figure 3. Relative Score of each Quality Assessor (DTM images) for a commons et of ≈1,000 products.

Table 1. Confusion matrix of quality (5-high and 1-low)

The quality was also assessed by the first author of a subset of ≈380 CTX DTMs. This used criteria such as no gaps or artefacts (Q5), small gaps, some jitter (Q4), larger gaps, worse jitter (Q3), large artefacts (Q4), lim-ited coverage and poor quality (Q1) and failed (Q0). The results are shown in Table 2. Note that more than three-quarters of the DTMs have either quality 5 or quality 4.

Table 2. Quality assessment of 3803 CTX DTMs based on a visual assessment.

01020304050

Quality

1

Quality

2

Quality

3

Quality

4

Quality

5

QA3

QA1

QA2

Pred

iction

Q1 50 0 1 0 091% 0% 0% 0% 0%

Q2 2 105 7 0 14% 79% 3% 0% 0%

Q3 3 20 207 33 85% 15% 78% 18% 3%

Q4 0 2 22 109 130% 2% 8% 61% 6%

Q5 0 6 27 38 2070% 5% 10% 21% 90%Q1 Q2 Q3 Q4 Q5

Truth

Quality N# %5 1852 48.70%4 1078 28.35%3 258 6.78%2 305 8.02%1 227 5.97%0 83 2.18%

7073.pdf4th Planetary Data Workshop 2019 (LPI Contrib. No. 2151)

A machine-learning based approach [6-7] employed a multi-class Radial Basis Function Support Vector Ma-chine (RBF SVM) classifier (see Figure 3 flowchart) to detect striping due to jitter. The results of this are shown in Table 3 below. Note the large ambiguity in the unsure class for the classifier and the high agreement for the strip detection.

Figure 3. flowchart showing how visual products employed to detect striping automatically in CTX DTMs.

Table 3. Statistics of automated stripe detection

Public distribution: The CTX DTMs are browsa-ble through the iMars webGIS system (http://www.i-mars.eu/web-gis) developed at the Freie Universität Berlin by [3]. Figure 4 shows an example webpage with the main control panels developed using OpenLayers and Figure 5 shows a pop-up menu to allow users to download products either from the original NASA PDS sites or new UCL products from the ESA Guest Storage Facility. This latter facility is available through a DOI landing page associated with a peer review publication on the datasets. The first of these is available for the new HRSC orbital DTMs and ORIs and their corresponding

mosaiced products for the Martian South Polar Residual Cap whose production is described in [8] and available through doi:10.5270/esa-0j79yk8

Figure 4. iMars webGIS showing main control functions.

Figure 5. Example showing how right-clicking can provide direct access to the datasets from their original source loca-tion on the NASA PDS or of derived products from the new ESA Guest Storage Facility.

References: [1] Tao Y. et al. (2018), Planetary and Space Science, 154, 30–58. [2] Muller, J-P., et al. (2019) this workshop. [3] Walter S. et al. (2018), ESS, 5, 308-323. [4] Kim, J.; Muller, J. (2009) Planetary and Space Science, 57, 2095–2112. [5] Gwinner, K. et al., (2016). Planetary and Space Science, 126, 93–138.[6] Muller et al., (2018), 20, EGU2018-15971. [7] Putri et al., (2018) EGU2018-1120. [8] Putri et al. (2019) Planetary and Space Science doi: 10.1016/j.pss.2019.02.010.

Acknowledgements: We would like to thank Kira Korbner and Akira for CTX DTM assessment and Mi-chael Landy for volunteering to assess the HRSC polar scenes prior to processing and CTX DTM assessment. The research leading to these results has received fund-ing from the European Un-ion’s Seventh Framework Programme (FP7/2007-2013) under iMars grant agree-ment n˚ 607379 and from the STFC “MSSL Consoli-dated Grant under “Planetary Surface Data Mining” ST/K000977/1. Further development work of the ESA Guest Storage Facility preparation is provided by the UK Space Agency Aurora support under project no. ST/S001891. Putri is supported by the Indonesian En-dowment Fund for Education.

TrainingTest Stripes No stripes/

unsure

Stripes2461/506 198/257

91.11% 77.04%

No stripes/ unsure

35/506 59/2578.89% 22.95%

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7073.pdf4th Planetary Data Workshop 2019 (LPI Contrib. No. 2151)