Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM Estimation and Super-Resolution Restoration
<p>Global coverage of CaSSIS in green (up to 28 August 2021) and HiRISE in blue (up to 2 September 2021) images of Mars (map projection: Mollweide).</p> "> Figure 2
<p>An overview map of the 9 test CaSSIS images (co-registered with the CTX and HRSC ORIs [<a href="#B28-remotesensing-14-00257" class="html-bibr">28</a>]), superimposed on the CTX MADNet DTM mosaic (baseline reference), over the ExoMars Rosalind Franklin rover’s landing site (1-sigma ellipse: red; 3-sigma ellipse: blue) at Oxia Planum [<a href="#B28-remotesensing-14-00257" class="html-bibr">28</a>,<a href="#B62-remotesensing-14-00257" class="html-bibr">62</a>,<a href="#B63-remotesensing-14-00257" class="html-bibr">63</a>] (centred at 18.239°N, 24.368°W; map projection: Mars Equirectangular).</p> "> Figure 3
<p>Network architectures of MARSGAN and MADNet.</p> "> Figure 4
<p>Flow diagram of the processing and evaluation methods.</p> "> Figure 5
<p>Overview maps of the 4 m/pixel CaSSIS NPB (NIR-PAN-BLU) band colour images, the resultant 1 m/pixel CaSSIS PAN band SRR single-strip images, 1 m/pixel CaSSIS SRR image mosaic, 1 m/pixel CaSSIS SRR image mosaic with HRSC colour, 8 m/pixel CaSSIS MADNet DTM mosaic, the shaded relief image (330° azimuth, 30° altitude, 2× vertical exaggeration) of the 8 m/pixel CaSSIS MADNet DTM mosaic, 2 m/pixel CaSSIS SRR MADNet DTM mosaic, and the shaded relief image (330° azimuth, 30° altitude, 2× vertical exaggeration) of the 2 m/pixel CaSSIS SRR MADNet DTM mosaic, over the 1-sigma (red) and 3-sigma (blue) ellipses of the ExoMars Rosalind Franklin rover’s planned landing site at Oxia Planum.</p> "> Figure 6
<p>Locations of the exemplar zoom-in areas that are demonstrated in and <a href="#sec3dot3-remotesensing-14-00257" class="html-sec">Section 3.3</a>. <b>Left</b>: 25 cm/pixel HiRISE PDS ORI (ESP_039299_1985_RED_A_01_ORTHO) superimposed on top of the resultant 1 m/pixel CaSSIS SRR image mosaic, superimposed by the 1-sigma (red) and 3-sigma (dark-blue) ellipses of the Rosalind Franklin ExoMars rover’s planned landing site at Oxia Planum; <b>Right</b>: multi-level zoom-in views of the same HiRISE PDS ORI.</p> "> Figure 7
<p>Visual comparisons of a small exemplar area (i.e., “Zoom-in Area-1”; location shown in <a href="#remotesensing-14-00257-f006" class="html-fig">Figure 6</a>) of the reference 25 cm/pixel HiRISE PDS ORI (ESP_039299_1985_RED_A_01_ORTHO), the resultant 1 m/pixel CaSSIS SRR image, the input 4 m/pixel CaSSIS PAN band image (MY34_004925_019_2_PAN), shaded relief images (using similar illumination parameters as the HiRISE PDS ORI, i.e., 225° azimuth, 30° altitude, 2× vertical exaggeration) of the resultant 8 m/pixel CaSSIS MADNet DTM, the resultant 2 m/pixel CaSSIS SRR MADNet DTM, the reference 1 m/pixel HiRISE PDS DTM (DTEEC_039299_1985_047501_1985_L01), and the reference 50 cm/pixel HiRISE MADNet DTM (from top to bottom).</p> "> Figure 8
<p>Visual comparisons of a small exemplar area (i.e., “Zoom-in Area-2”–a subarea of “Zoom-in Area-1” that is shown in <a href="#remotesensing-14-00257-f007" class="html-fig">Figure 7</a>; location is shown in <a href="#remotesensing-14-00257-f006" class="html-fig">Figure 6</a>) of the input 25 cm/pixel HiRISE PDS ORI (ESP_039299_1985_RED_A_01_ORTHO), the resultant 6.25 cm/pixel HiRISE SRR image, the shaded relief images (using similar illumination parameters as the HiRISE PDS ORI, i.e., 225° azimuth, 30° altitude, 2× vertical exaggeration) of the reference 1 m/pixel HiRISE PDS DTM (DTEEC_039299_1985_047501_1985_L01), the reference 50 cm/pixel HiRISE MADNet DTM, and the resultant 12.5 cm/pixel HiRISE SRR MADNet DTM (from top to bottom).</p> "> Figure 9
<p><b>Left</b>: RMSE and SSIM plots of the target 18 m/pixel CASP-GO CTX DTM (J01_045167_1983_XN_18N024W-J03_045800_1983_XN_18N024W-DTM [<a href="#B28-remotesensing-14-00257" class="html-bibr">28</a>]) and the reference 1 m/pixel HiRISE PDS DTM (DTEEC_039299_1985_047501_1985_L01); <b>Right</b>: RMSE and SSIM plots of the target 25 m/pixel HRSC MADNet DTM [<a href="#B28-remotesensing-14-00257" class="html-bibr">28</a>] and the reference 1 m/pixel HiRISE PDS DTM (DTEEC_039299_1985_047501_1985_L01).</p> "> Figure 10
<p><b>Left</b>: RMSE and SSIM plots of the target 8 m/pixel CaSSIS MADNet DTM and the reference 12.5 cm/pixel HiRISE SRR MADNet DTM; <b>Right</b>: RMSE and SSIM plots of the target 2 m/pixel CaSSIS SRR MADNet DTM and the reference 12.5 cm/pixel HiRISE SRR MADNet DTM.</p> "> Figure 11
<p><b>Top</b>: RMSE and SSIM plots of the target 1 m/pixel HiRISE PDS DTM (DTEEC_039299_1985_047501_1985_L01) and the reference 12.5 cm/pixel HiRISE SRR MADNet DTM; <b>Bottom-left</b>: RMSE and SSIM plots of the target 12 m/pixel CTX MADNet DTM [<a href="#B28-remotesensing-14-00257" class="html-bibr">28</a>] and the reference 12.5 cm/pixel HiRISE SRR MADNet DTM; <b>Bottom-right</b>: RMSE and SSIM plots of the target 50 cm/pixel HiRISE MADNet DTM [<a href="#B28-remotesensing-14-00257" class="html-bibr">28</a>] and the reference 12.5 cm/pixel HiRISE SRR MADNet DTM.</p> "> Figure 12
<p>Images and shaded relief images of the corresponding DTM products showing a portion (south-west corner) of a small crater (centred at 24.3350° W, 18.0716° N) of the test site at Oxia Planum. 1st row: 4 m/pixel CaSSIS PAN band image (MY35_009481_165_0_PAN), 1 m/pixel CaSSIS PAN band SRR image, and 25 cm/pixel HiRISE RED band PDS ORI (ESP_039299_1985_RED_A_01_ORTHO); 2nd row: shaded relief images (using similar illumination parameters as the HiRISE PDS ORI, i.e., 225° azimuth, 30° altitude, 2× vertical exaggeration) of the 8 m/pixel CaSSIS MADNet DTM, 2 m/pixel CaSSIS SRR MADNet DTM, and 50 cm/pixel HiRISE MADNet DTM. Red arrows point to an exemplar of the super-resolved features and their topography that are considered less accurate compared to the HiRISE images and DTMs. Green arrows point to an exemplar of the super-resolved features and their topography that are considered more accurate compared to the HiRISE images and DTMs.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Datasets
2.2. Overview of MARSGAN SRR and MADNet SDE
2.3. DTM Evaluation
3. Results
3.1. Overview of the CaSSIS SRR and SDE Results and Product Access Information
3.2. Qualitative Assessment of the CaSSIS SRR ORI and SRR MADNet DTM
3.3. Qualitative Assessments of the HiRISE SRR and SRR MADNet DTMs
3.4. Quantitative Assessment of the CaSSIS SRR MADNet DTM
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Leighton, R.B.; Murray, B.C.; Sharp, R.P.; Allen, J.D.; Sloan, R.K. Mariner IV photography of Mars: Initial results. Science 1965, 149, 627–630. [Google Scholar] [CrossRef] [PubMed]
- Albee, A.L.; Arvidson, R.E.; Palluconi, F.; Thorpe, T. Overview of the Mars global surveyor mission. J. Geophys. Res. Planets 2001, 106, 23291–23316. [Google Scholar] [CrossRef] [Green Version]
- Schmidt, R. Mars Express—ESA’s first mission to planet Mars. Acta Astronaut. 2003, 52, 197–202. [Google Scholar] [CrossRef]
- Vago, J.; Witasse, O.; Svedhem, H.; Baglioni, P.; Haldemann, A.; Gianfiglio, G.; Blancquaert, T.; McCoy, D.; de Groot, R. ESA ExoMars program: The next step in exploring Mars. Sol. Syst. Res. 2015, 49, 518–528. [Google Scholar] [CrossRef]
- Zurek, R.W.; Smrekar, S.E. An overview of the Mars Reconnaissance Orbiter (MRO) science mission. J. Geophys. Res. Planets 2007, 112, E05S01. [Google Scholar] [CrossRef] [Green Version]
- Zou, Y.; Zhu, Y.; Bai, Y.; Wang, L.; Jia, Y.; Shen, W.; Fan, Y.; Liu, Y.; Wang, C.; Zhang, A.; et al. Scientific objectives and payloads of Tianwen-1, China’s first Mars exploration mission. Adv. Space Res. 2021, 67, 812–823. [Google Scholar] [CrossRef]
- Golombek, M.P. The mars pathfinder mission. J. Geophys. Res. Planets 1997, 102, 3953–3965. [Google Scholar] [CrossRef]
- Wright, I.P.; Sims, M.R.; Pillinger, C.T. Scientific objectives of the Beagle 2 lander. Acta Astronaut. 2003, 52, 219–225. [Google Scholar] [CrossRef]
- Crisp, J.A.; Adler, M.; Matijevic, J.R.; Squyres, S.W.; Arvidson, R.E.; Kass, D.M. Mars exploration rover mission. J. Geophys. Res. Planets 2003, 108, 8061. [Google Scholar] [CrossRef]
- Grotzinger, J.P.; Crisp, J.; Vasavada, A.R.; Anderson, R.C.; Baker, C.J.; Barry, R.; Blake, D.F.; Conrad, P.; Edgett, K.S.; Ferdowski, B.; et al. Mars Science Laboratory mission and science investigation. Space Sci. Rev. 2012, 170, 5–56. [Google Scholar] [CrossRef] [Green Version]
- Smith, D.E.; Zuber, M.T.; Frey, H.V.; Garvin, J.B.; Head, J.W.; Muhleman, D.O.; Pettengill, G.H.; Phillips, R.J.; Solomon, S.C.; Zwally, H.J.; et al. Mars Orbiter Laser Altimeter—Experiment summary after the first year of global mapping of Mars. J. Geophys. Res. 2001, 106, 23689–23722. [Google Scholar] [CrossRef]
- Neumann, G.A.; Rowlands, D.D.; Lemoine, F.G.; Smith, D.E.; Zuber, M.T. Crossover analysis of Mars Orbiter Laser Altimeter data. J. Geophys. Res. 2001, 106, 23753–23768. [Google Scholar] [CrossRef] [Green Version]
- Neukum, G.; Jaumann, R. HRSC: The high resolution stereo camera of Mars Express. Sci. Payload 2004, 1240, 17–35. [Google Scholar]
- Malin, M.C.; Bell, J.F.; Cantor, B.A.; Caplinger, M.A.; Calvin, W.M.; Clancy, R.T.; Edgett, K.S.; Edwards, L.; Haberle, R.M.; James, P.B.; et al. Context camera investigation on board the Mars Reconnaissance Orbiter. J. Geophys. Res. Space Phys. 2007, 112, 112. [Google Scholar] [CrossRef] [Green Version]
- Thomas, N.; Cremonese, G.; Ziethe, R.; Gerber, M.; Brändli, M.; Bruno, G.; Erismann, M.; Gambicorti, L.; Gerber, T.; Ghose, K.; et al. The colour and stereo surface imaging system (CaSSIS) for the ExoMars trace gas orbiter. Space Sci. Rev. 2017, 212, 1897–1944. [Google Scholar] [CrossRef] [Green Version]
- Tornabene, L.L.; Seelos, F.P.; Pommerol, A.; Thomas, N.; Caudill, C.M.; Becerra, P.; Bridges, J.C.; Byrne, S.; Cardinale, M.; Chojnacki, M.; et al. Image simulation and assessment of the colour and spatial capabilities of the Colour and Stereo Surface Imaging System (CaSSIS) on the ExoMars Trace Gas Orbiter. Space Sci. Rev. 2018, 214, 1–61. [Google Scholar] [CrossRef]
- McEwen, A.S.; Eliason, E.M.; Bergstrom, J.W.; Bridges, N.T.; Hansen, C.J.; Delamere, W.A.; Grant, J.A.; Gulick, V.C.; Herkenhoff, K.E.; Keszthelyi, L.; et al. Mars reconnaissance orbiter’s high resolution imaging science experiment (HiRISE). J. Geophys. Res. Space Phys. 2007, 112, E05S02. [Google Scholar] [CrossRef] [Green Version]
- Gwinner, K.; Jaumann, R.; Hauber, E.; Hoffmann, H.; Heipke, C.; Oberst, J.; Neukum, G.; Ansan, V.; Bostelmann, J.; Dumke, A.; et al. The High Resolution Stereo Camera (HRSC) of Mars Express and its approach to science analysis and mapping for Mars and its satellites. Planet. Space Sci. 2016, 126, 93–138. [Google Scholar] [CrossRef]
- Beyer, R.; Alexandrov, O.; McMichael, S. The Ames Stereo Pipeline: NASA’s Opensource Software for Deriving and Processing Terrain Data. Earth Space Sci. 2018, 5, 537–548. [Google Scholar] [CrossRef]
- Tao, Y.; Muller, J.P.; Sidiropoulos, P.; Xiong, S.T.; Putri, A.R.D.; Walter, S.H.G.; Veitch-Michaelis, J.; Yershov, V. Massive stereo-based DTM production for Mars on cloud computers. Planet. Space Sci. 2018, 154, 30–58. [Google Scholar] [CrossRef]
- Tao, Y.; Michael, G.; Muller, J.P.; Conway, S.J.; Putri, A.R. Seamless 3 D Image Mapping and Mosaicing of Valles Marineris on Mars Using Orbital HRSC Stereo and Panchromatic Images. Remote Sens. 2021, 13, 1385. [Google Scholar] [CrossRef]
- Douté, S.; Jiang, C. Small-Scale Topographical Characterization of the Martian Surface with In-Orbit Imagery. IEEE Trans. Geosci. Remote Sens. 2019, 58, 447–460. [Google Scholar] [CrossRef]
- Tyler, L.; Cook, T.; Barnes, D.; Parr, G.; Kirk, R. Merged shape from shading and shape from stereo for planetary topographic mapping. In Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria, 27 April–2 May 2014; p. 16110. [Google Scholar]
- Hess, M. High Resolution Digital Terrain Model for the Landing Site of the Rosalind Franklin (ExoMars) Rover. Adv. Space Res. 2019, 53, 1735–1767. [Google Scholar]
- Tao, Y.; Douté, S.; Muller, J.-P.; Conway, S.J.; Thomas, N.; Cremonese, G. Ultra-High-Resolution 1 m/pixel CaSSIS DTM Using Super-Resolution Restoration and Shape-from-Shading: Demonstration over Oxia Planum on Mars. Remote Sens. 2021, 13, 2185. [Google Scholar] [CrossRef]
- Chen, Z.; Wu, B.; Liu, W.C. Mars3DNet: CNN-Based High-Resolution 3D Reconstruction of the Martian Surface from Single Images. Remote Sens. 2021, 13, 839. [Google Scholar] [CrossRef]
- Tao, Y.; Xiong, S.; Conway, S.J.; Muller, J.-P.; Guimpier, A.; Fawdon, P.; Thomas, N.; Cremonese, G. Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets. Remote Sens. 2021, 13, 2877. [Google Scholar] [CrossRef]
- Tao, Y.; Muller, J.-P.; Conway, S.J.; Xiong, S. Large Area High-Resolution 3D Mapping of Oxia Planum: The Landing Site for the ExoMars Rosalind Franklin Rover. Remote Sens. 2021, 13, 3270. [Google Scholar] [CrossRef]
- Tao, Y.; Muller, J.-P.; Xiong, S.; Conway, S.J. MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning. Remote Sens. 2021, 13, 4220. [Google Scholar] [CrossRef]
- Tao, Y.; Conway, S.J.; Muller, J.-P.; Putri, A.R.D.; Thomas, N.; Cremonese, G. Single Image Super-Resolution Restoration of TGO CaSSIS Colour Images: Demonstration with Perseverance Rover Landing Site and Mars Science Targets. Remote Sens. 2021, 13, 1777. [Google Scholar] [CrossRef]
- Li, X.; Hu, Y.; Gao, X.; Tao, D.; Ning, B. A multi-frame image super-resolution method. Signal Process. 2010, 90, 405–414. [Google Scholar] [CrossRef]
- Farsiu, S.; Robinson, M.D.; Elad, M.; Milanfar, P. Fast and robust multiframe super resolution. IEEE Trans. Image Process. 2004, 13, 1327–1344. [Google Scholar] [CrossRef]
- Tao, Y.; Muller, J.P. A novel method for surface exploration: Super-resolution restoration of Mars repeat-pass orbital imagery. Planet. Space Sci. 2016, 121, 103–114. [Google Scholar] [CrossRef] [Green Version]
- Tao, Y.; Muller, J.-P. Super-Resolution Restoration of Spaceborne Ultra-High-Resolution Images Using the UCL OpTiGAN System. Remote Sens. 2021, 13, 2269. [Google Scholar] [CrossRef]
- Tao, Y.; Xiong, S.; Song, R.; Muller, J.-P. Towards Streamlined Single-Image Super-Resolution: Demonstration with 10 m Sentinel-2 Colour and 10–60 m Multi-Spectral VNIR and SWIR Bands. Remote Sens. 2021, 13, 2614. [Google Scholar] [CrossRef]
- Tao, Y.; Muller, J.-P. Super-Resolution Restoration of MISR Images Using the UCL MAGiGAN System. Remote Sens. 2019, 11, 52. [Google Scholar] [CrossRef] [Green Version]
- Lim, B.; Son, S.; Kim, H.; Nah, S.; Mu Lee, K. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, 21–26 July 2017; pp. 136–144. [Google Scholar]
- Yu, J.; Fan, Y.; Yang, J.; Xu, N.; Wang, Z.; Wang, X.; Huang, T. Wide Activation for Efficient and Accurate Image Super-Resolution. arXiv 2018, arXiv:1808.08718. [Google Scholar]
- Ahn, N.; Kang, B.; Sohn, K.A. Fast, accurate, and lightweight super-resolution with cascading residual network. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 252–268. [Google Scholar]
- Kim, J.; Lee, J.K.; Lee, K.M. Deeply-recursive convolutional network for image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 1637–1645. [Google Scholar]
- Tai, Y.; Yang, J.; Liu, X. Image super-resolution via deep recursive residual network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 3147–3155. [Google Scholar]
- Wang, C.; Li, Z.; Shi, J. Lightweight Image Super-Resolution with Adaptive Weighted Learning Network. arXiv 2019, arXiv:1904.02358. [Google Scholar]
- Zhang, Y.; Li, K.; Li, K.; Wang, L.; Zhong, B.; Fu, Y. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 286–301. [Google Scholar]
- Ledig, C.; Theis, L.; Huszár, F.; Caballero, J.; Cunningham, A.; Acosta, A.; Aitken, A.; Tejani, A.; Totz, J.; Wang, Z.; et al. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 4681–4690. [Google Scholar]
- Sajjadi, M.S.; Scholkopf, B.; Hirsch, M. EnhanceNet: Single image super-resolution through automated texture synthesis. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017; pp. 4491–4500. [Google Scholar]
- Wang, X.; Yu, K.; Wu, S.; Gu, J.; Liu, Y.; Dong, C.; Qiao, Y.; Change Loy, C. ESRGAN: Enhanced super-resolution generative adversarial networks. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany, 8–14 September 2018. [Google Scholar]
- Kirk, R.A. Fast Finite-Element Algorithm for Two-Dimensional Photoclinometry. Ph.D. Thesis, California Institute of Technology, Pasadena, CA, USA, 1987. [Google Scholar]
- Liu, W.C.; Wu, B. An integrated photogrammetric and photoclinometric approach for illumination-invariant pixel-resolution 3D mapping of the lunar surface. ISPRS J. Photogramm. Remote Sens. 2020, 159, 153–168. [Google Scholar] [CrossRef]
- Shelhamer, E.; Barron, J.T.; Darrell, T. Scene intrinsics and depth from a single image. In Proceedings of the IEEE International Conference on Computer Vision Workshops, Santiago, Chile, 7–13 December 2015; pp. 37–44. [Google Scholar]
- Ma, X.; Geng, Z.; Bie, Z. Depth Estimation from Single Image Using CNN-Residual Network. SemanticScholar. 2017. Available online: http://cs231n.stanford.edu/reports/2017/pdfs/203.pdf (accessed on 5 January 2022).
- Laina, I.; Rupprecht, C.; Belagiannis, V.; Tombari, F.; Navab, N. Deeper depth prediction with fully convolutional residual networks. In Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, 25–28 October 2016; pp. 239–248. [Google Scholar]
- Li, B.; Shen, C.; Dai, Y.; van den Hengel, A.; He, M. Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015; pp. 1119–1127. [Google Scholar]
- Liu, F.; Shen, C.; Lin, G.; Reid, I. Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans. Pattern Anal. Mach. Intell. 2015, 38, 2024–2039. [Google Scholar] [CrossRef] [Green Version]
- Wang, P.; Shen, X.; Lin, Z.; Cohen, S.; Price, B.; Yuille, A.L. Towards unified depth and semantic prediction from a single image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA, 17–19 June 2015; pp. 2800–2809. [Google Scholar]
- Mousavian, A.; Pirsiavash, H.; Košecká, J. Joint semantic segmentation and depth estimation with deep convolutional networks. In Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, 25–28 October 2016; pp. 611–619. [Google Scholar]
- Xu, D.; Wang, W.; Tang, H.; Liu, H.; Sebe, N.; Ricci, E. Structured attention guided convolutional neural fields for monocular depth estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015; pp. 3917–3925. [Google Scholar]
- Chen, Y.; Zhao, H.; Hu, Z.; Peng, J. Attention-based context aggregation network for monocular depth estimation. Int. J. Mach. Learn. Cybern. 2021, 12, 1583–1596. [Google Scholar] [CrossRef]
- Jung, H.; Kim, Y.; Min, D.; Oh, C.; Sohn, K. Depth prediction from a single image with conditional adversarial networks. In Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 17–20 September 2017; pp. 1717–1721. [Google Scholar]
- Lore, K.G.; Reddy, K.; Giering, M.; Bernal, E.A. Generative adversarial networks for depth map estimation from RGB video. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, 18–22 June 2018; pp. 1258–12588. [Google Scholar]
- Lee, J.H.; Han, M.K.; Ko, D.W.; Suh, I.H. From big to small: Multi-scale local planar guidance for monocular depth estimation. arXiv 2019, arXiv:1907.10326. [Google Scholar]
- Wofk, D.; Ma, F.; Yang, T.J.; Karaman, S.; Sze, V. Fastdepth: Fast monocular depth estimation on embedded systems. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20–24 May 2019; pp. 6101–6108. [Google Scholar]
- Quantin-Nataf, C.; Carter, J.; Mandon, L.; Thollot, P.; Balme, M.; Volat, M.; Pan, L.; Loizeau, D.; Millot, C.; Breton, S.; et al. Oxia Planum: The Landing Site for the ExoMars “Rosalind Franklin” Rover Mission: Geological Context and Prelanding Interpretation. Astrobiology 2021, 21, 345–366. [Google Scholar] [CrossRef]
- Fawdon, P.; Grindrod, P.; Orgel, C.; Sefton-Nash, E.; Adeli, S.; Balme, M.; Cremonese, G.; Davis, J.; Frigeri, A.; Hauber, E.; et al. The geography of Oxia Planum. J. Maps 2021, 17, 752–768. [Google Scholar] [CrossRef]
- Kirk, R.L.; Mayer, D.P.; Fergason, R.L.; Redding, B.L.; Galuszka, D.M.; Hare, T.M.; Gwinner, K. Evaluating Stereo Digital Terrain Model Quality at Mars Rover Landing Sites with HRSC, CTX, and HiRISE Images. Remote Sens. 2021, 13, 3511. [Google Scholar] [CrossRef]
- Wang, Z.; Bovik, A.C.; Sheikh, H.R.; Simoncelli, E.P. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 2004, 13, 600–612. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Michael, G.G.; Walter, S.H.G.; Kneissl, T.; Zuschneid, W.; Gross, C.; McGuire, P.C.; Dumke, A.; Schreiner, B.; van Gasselt, S.; Gwinner, K.; et al. Systematic processing of Mars Express HRSC panchromatic and colour image mosaics: Image equalisation using an external brightness reference. Planet. Space Sci. 2016, 121, 18–26. [Google Scholar] [CrossRef] [Green Version]
- Traxler, C.; Ortner, T. PRo3D—A tool for remote exploration and visual analysis of multi-resolution planetary terrains. In Proceedings of the European Planetary Science Congress, Nantes, France, 27 September–2 October 2015; pp. EPSC2015–EPSC2023. [Google Scholar]
- Barnes, R.; Gupta, S.; Traxler, C.; Ortner, T.; Bauer, A.; Hesina, G.; Paar, G.; Huber, B.; Juhart, K.; Fritz, L.; et al. Geological analysis of Martian rover-derived digital outcrop models using the 3-D visualization tool, Planetary Robotics 3-D Viewer—Pro3D. Earth Space Sci. 2018, 5, 285–307. [Google Scholar] [CrossRef]
- Muller, J.P.; Tao, Y.; Putri, A.R.D.; Watson, G.; Beyer, R.; Alexandrov, O.; McMichael, S.; Besse, S.; Grotheer, E. 3D Imaging tools and geospatial services from joint European-USA collaborations. In Proceedings of the European Planetary Science Conference Jointly Held with the US DPS, EPSC–DPS2019–1355–2, Spokane, WA, USA, 25–30 October 2019; Volume 13. [Google Scholar]
- Masson, A.; de Marchi, G.; Merin, B.; Sarmiento, M.H.; Wenzel, D.L.; Martinez, B. Google dataset search and DOI for data in the ESA space science archives. Adv. Space Res. 2021, 67, 2504–2516. [Google Scholar] [CrossRef]
- Sefton-Nash, E.; Fawdon, P.; Orgel, C.; Balme, M.; Quantin-Nataf, C.; Volat, M.; Hauber, E.; Adeli, S.; Davis, J.; Grindrod, P.; et al. Exomars RSOWG. Team mapping of oxia planum for the exomars 2022 rover-surface platform mission. In Proceedings of the Liquid Propulsion Systems Centre 2021, Thiruvananthapuram, India, 19–30 April 2021; Volume 52, p. 1947. [Google Scholar]
- Goodfellow, I.J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. Generative adversarial networks. arXiv 2014, arXiv:1406.2661. [Google Scholar] [CrossRef]
- Jolicoeur-Martineau, A. The relativistic discriminator: A key element missing from standard GAN. arxiv 2018, arXiv:1807.00734. [Google Scholar]
- Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K.Q. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 4700–4708. [Google Scholar]
- Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 18 May 2015; pp. 234–241. [Google Scholar]
- Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR). arXiv 2014, arXiv:1409.1556. [Google Scholar]
- Cai, J.; Zeng, H.; Yong, H.; Cao, Z.; Zhang, L. Toward real-world single image super-resolution: A new benchmark and a new model. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 27–28 October 2019; pp. 3086–3095. [Google Scholar]
- Zwald, L.; Lambert-Lacroix, S. The berhu penalty and the grouped effect. arXiv 2012, arXiv:1207.6868. [Google Scholar]
Input CaSSIS Image ID | Overlapping HiRISE PDS DTM ID | HiRISE PDS ORI ID |
---|---|---|
MY35_007250_019_0 | - | - |
MY35_007623_019_0 | - | - |
MY35_008742_019_0 (cropped) | - | - |
MY34_003806_019_2 | DTEEC_039299_1985_047501_1985_L01 | ESP_039299_1985_RED_A_01_ORTHO |
MY34_004925_019_2 | DTEEC_036925_1985_037558_1985_L01 | ESP_036925_1985_RED_A_01_ORTHO |
MY35_009481_165_0 (cropped) | - | - |
MY35_013584_163_0 (cropped) | DTEEC_042134_1985_053962_1985_L01 | ESP_042134_1985_RED_A_01_ORTHO |
MY34_005664_163_2 | - | - |
MY34_005012_018_2 | DTEEC_037070_1985_037136_1985_L01 | ESP_037070_1985_RED_A_01_ORTHO |
Target DTM | Best Fit Filter Width (Pixels) for RMSE | RMSE (m) | Best Fit Filter Width (Pixels) for SSIM | SSIM (0–1) | Nominal Resolution | Estimated Resolution |
---|---|---|---|---|---|---|
CTX MADNet DTM | 33 × 33 | 3.480 | 5 × 5 | 0.807 | 12 m | - |
CaSSIS MADNet DTM | 27 × 27 | 1.553 | 27 × 27 | 0.657 | 8 m | 9–10 m |
HiRISE PDS DTM | 17 × 17 | 0.228 | 17 × 17 | 0.902 | 1 m | 4–5 m |
CaSSIS SRR MADNet DTM | 9 × 9 | 1.876 | 11 × 11 | 0.607 | 2 m | 1–2 m |
HiRISE MADNet DTM | 7 × 7 | 0.224 | 7 × 7 | 0.917 | 50 cm | - |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tao, Y.; Xiong, S.; Muller, J.-P.; Michael, G.; Conway, S.J.; Paar, G.; Cremonese, G.; Thomas, N. Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM Estimation and Super-Resolution Restoration. Remote Sens. 2022, 14, 257. https://doi.org/10.3390/rs14020257
Tao Y, Xiong S, Muller J-P, Michael G, Conway SJ, Paar G, Cremonese G, Thomas N. Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM Estimation and Super-Resolution Restoration. Remote Sensing. 2022; 14(2):257. https://doi.org/10.3390/rs14020257
Chicago/Turabian StyleTao, Yu, Siting Xiong, Jan-Peter Muller, Greg Michael, Susan J. Conway, Gerhard Paar, Gabriele Cremonese, and Nicolas Thomas. 2022. "Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM Estimation and Super-Resolution Restoration" Remote Sensing 14, no. 2: 257. https://doi.org/10.3390/rs14020257