Earth, Moon, and Planets

, Volume 121, Issue 1–2, pp 59–72 | Cite as

Remote Sensing of Mars: Detection of Impact Craters on the Mars Global Surveyor DTM by Integrating Edge- and Region-Based Algorithms

  • C. D. Athanassas
  • A. Vaiopoulos
  • P. Kolokoussis
  • D. Argialas
Article
  • 60 Downloads

Abstract

This study integrates two different computer vision approaches, namely the circular Hough transform (CHT) and the determinant of Hessian (DoH), to detect automatically the largest number possible of craters of any size on the digital terrain model (DTM) generated by the Mars Global Surveyor mission. Specifically, application of the standard version of CHT to the DTM captured a great number of craters with diameter smaller than ~ 50 km only, failing to capture larger craters. On the other hand, DoH was successful in detecting craters that were undetected by CHT, but its performance was deterred by the irregularity of the topographic surface encompassed: strongly undulated and inclined (trended) topographies hindered crater detection. When run on a de-trended DTM (and keeping the topology unaltered) DoH scored higher. Current results, although not optimal, encourage combined use of CHT and DoH for routine crater detection undertakings.

Keywords

Circular Hough transform Derivative of hessian Blobs Computer vision Digital terrain model 

Notes

Acknowledgements

The authors thank an anonymous reviewer for providing constructive comments.

References

  1. J.C. Andrews-Hanna, R.J. Phillips, M.T. Zuber, Meridiani Planum and the global hydrology of Mars. Nature 446, 163–166 (2007)ADSCrossRefGoogle Scholar
  2. D. Argialas, S. Krishnamoorthy, Detection of lines and circles in maps and engineering drawings, in International Archives of Photogrammetry and Remote Sensing, Vol. XXIX, part B, Commision III, ed. by L.W. Fritz, J.R. Lucas, pp. 392–399 (1992)Google Scholar
  3. T.J. Atheron, D.J. Kerbyson, Size invariant circle detection. Image Vis. Comput. 17(11), 795–803 (1999)CrossRefGoogle Scholar
  4. H. Bay, A. Ess, T. Tuytelaars, L. van Gool, Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008)CrossRefGoogle Scholar
  5. N.G. Barlow, Stones, Wind and Ice (Lunar and Planetary Institute, 2016). Retrieved 19 Mar 2016. http://www.lpi.usra.edu/publications/slidesets/stones/
  6. U. Bayer, Pattern Recognition Problems in Geology and Paleontology (Springer, Berlin, 1985), p. 229MATHGoogle Scholar
  7. D.C. Berman, D.A. Crown, L.F. Bleamaster III, Degradation of mid-latitude craters on Mars. Icarus 200, 77–95 (2009)ADSCrossRefGoogle Scholar
  8. T. Brunetti, F. Guzzetti, M. Cardinali, F. Fiorucci, M. Santangelo, P. Mancinelli, G. Komatsu, L. Borselli, Analysis of a new geomorphological inventory of landslides in Valles Marineris, Mars. Earth Planet. Sci. Lett. 405, 156–168 (2014)ADSCrossRefGoogle Scholar
  9. B.D. Bue, T.F. Stepinski, Machine detection of Martian impact craters from digital topography. IEEE Trans. Geosci. Remote Sens. 45, 265–274 (2007)ADSCrossRefGoogle Scholar
  10. M.H. Carr, J.W. Head, Geologic history of Mars. Earth Planet. Sci. Lett. 294, 185–203 (2010)ADSCrossRefGoogle Scholar
  11. R.O. Duda, P.E. Hart, Use of the Hough transform to detect lines and curves in pictures. Graph. Image Process. 15, 11–15 (1972)MATHGoogle Scholar
  12. R. Gomes, H.F. Levison, K. Tsiganis, A. Morbidelli, Origin of the cataclysmic Late Heavy Bombardment period of the terrestrial planets. Nature 435, 466–469 (2005)ADSCrossRefGoogle Scholar
  13. K. Grauman, B. Leibe, Visual Object Recognition (Morgan & Claypool, San Rafael, 2011), p. 163Google Scholar
  14. R.M. Haralick, L.G. Shapiro, Glossary of computer vision terms. Pattern Recogn. 24, 69–93 (1991)CrossRefGoogle Scholar
  15. W.K. Hartmann, G. Neukum, Cratering chronology and evolution of Mars, in Chronology and Evolution of Mars. Space Science Reviews, Vol. 12 ed. R. Kallenbach et al. (2001), pp. 105–164Google Scholar
  16. J. Illingworth, J. Kittler, The adaptive Hough transform. IEEE Trans. Pattern Anal. Mach. Intell. 9, 690–698 (1987)CrossRefGoogle Scholar
  17. K.G. Karantzalos, D.P. Argialas, Towards automatic olive tree extraction from satellite imagery. Geo-Imagery Bridging Continents. XXth ISPRS Congress (2004), pp. 12–23Google Scholar
  18. A. Kaspers, Blob Detction. Unpublished Master’s thesis, Utrecht University (2011)Google Scholar
  19. J.R. Kim, J.P. Muller, S. van Gasselt, J. Morley, G. Neukum, HRSC CoI Team, Automated crater detection, a new tool for Mars cartography and chronology. Photogramm. Eng. Remote Sens. 71, 1205–1217 (2005)CrossRefGoogle Scholar
  20. T. Lindeberg, Detecting salient blob-like image structures and their scales with a scale-space primal sketch: a method for focus-of-attention. Int. J. Comput. Vision 11, 283–318 (1993)CrossRefGoogle Scholar
  21. T. Lindeberg, Feature detection with automatic scale selection. Int. J. Comput. Vision 30, 79–116 (1998)CrossRefGoogle Scholar
  22. V.Y. Mariano, J. Min, J.H. Park, R. Kasturi, D. Mihalcik, H. Li, D. Doermann, T. Drayer, Performance evaluation of object detection algorithms. Proc. Int. Conf. Pattern Recognit. 3, 965–969 (2002)Google Scholar
  23. K. Mikolajczyk, C. Schmid, Scale and affine invariant interest point detectors. Int. J. Comput. Vision 60, 63–86 (2004)CrossRefGoogle Scholar
  24. Z. Mingzhu, C. Huanrong, A new method of circle’s center and radius detection in image processing. Proceedings of the IEEE Int. Conf. on Automation and Logistics, China (2008), pp. 2239–2242Google Scholar
  25. G. Neukum, K. Hiller, Martian ages. J. Geophys. Res. 86, 3097–3121 (1981)ADSCrossRefGoogle Scholar
  26. Z. Xiao, P. Weij, Detection of circle based on Hough transform. Transducer Micro Syst. Technol. 8, 25–34 (2006)Google Scholar
  27. R. Pyle, Destination Mars (Prometheus Books, Amherst. p, 2012), p. 348Google Scholar
  28. J. Shan, S. Lee, Quality of building extraction from IKONOS imagery. J. Surv. Eng. 131, 27–32 (2005)CrossRefGoogle Scholar
  29. T.F. Stepinski, S. Ghosh, R. Vilalta, Automatic recognition of landforms on Mars using terrain segmentation and classification, in DS 2006, LNAI ed. by N. Lavrač, L. Todorovski, K.P. Jantke, 2006, pp. 255–266Google Scholar
  30. C. Wiedemann, C. Heipke, H. Mayer, S. Hinz, Automatic extraction and evaluation of road networks from MOMS-2P imagery. in International Archives of Photogrammetry and Remote Sensing, Vol. 30, No. 3/1, pp. 285–291 (1998)Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • C. D. Athanassas
    • 1
    • 2
  • A. Vaiopoulos
    • 1
  • P. Kolokoussis
    • 1
  • D. Argialas
    • 1
  1. 1.Remote Sensing Laboratory, School of Rural and Surveying EngineeringNational Technical University of Athens (NTUA)Zografou, AthensGreece
  2. 2.Department of Geological Sciences, School of Mining and Metallurgical EngineeringNTUAZografou, AthensGreece

Personalised recommendations