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Shadow–highlight feature matching automatic small crater recognition using high-resolution digital orthophoto map from Chang’E Missions

  • Wei Zuo
  • Chunlai LiEmail author
  • Lingjie Yu
  • Zhoubin Zhang
  • Rongwu Wang
  • Xingguo Zeng
  • Yuxuan Liu
  • Yaying Xiong
Original Article
  • 22 Downloads

Abstract

This paper introduces a new method of small lunar craters’ automatic identification, using digital orthophoto map (DOM) data. The core of the approach is the fact that the lunar exploration DOM data reveal contrasting highlight and shadow characteristics of small craters under sunlight irradiation. This research effort combines image processing and mathematical modeling. Overall it proposes a new planetary data processing approach, to segment and extract the highlight and shadow regions of small craters, using the image gray frequency (IGF) statistical method. IGF can also be applied to identify the coupling relationships between small craters’ shape and their relative features. This paper presents the highlight and shadow pair matching (HSPM) model which manages to perform high-precision automatic recognition of small lunar craters. Testing was performed using the DOM data of Chang’E-2 (CE-2). The results have shown that the proposed method has a high level of successful detection rate. The proposed methodology that uses DOM data can complement the drawbacks of the digital elevation model (DEM) that has a relatively high false detection rate. A hybrid fusion model (FUM) that combines both DOM and DEM data, was carried out to simultaneously identify small, medium, and large-sized craters. It has been proven that the FUM generally shows stronger recognition ability compared to previous approaches and it can be adapted for high precision identification of craters on the whole lunar surface. The results meet the requirements for a reliable and accurate exploration of the Moon and the planets.

Keywords

Moon Crater recognition Image processing Space exploration 

Notes

Acknowledgements

Funding was provided by National Major Projects-GRAS Construction of China Lunar Exploration Project and Nation Science Foundation Project (No. 41671458).

References

  1. Bandeira L, Saraiva J, Pina P (2007) Development of a methodology for automated crater detection on planetary image. Lect Notes Comput Sci 4477:193–200CrossRefGoogle Scholar
  2. Barata T, Alves EI, Saraiva J, Pina P (2004) Automatic recognition of impact craters on the surface of Mars. In: Campilho A, Kamel M (eds) Image analysis and recognition. Lecture notes in computer science, vol 3212. Springer, Berlin, pp 489–496CrossRefGoogle Scholar
  3. Bierhaus EB, Merline WJ, Chapman CR, Burl MC (2001) Characterization of secondary craters using machine vision. In: Proceedings of the 6th international symposium on artificial intelligence, Robotics, and automation in space, p AM114Google Scholar
  4. Bruzzone L, Lizzi L, Marchetti PG (2004) Recognition and detection of impact craters from EO products. In: Proceedings of the ESA-EUSC 2004-theory and applications of knowledge-driven image information mining with focus on earth observation, Madrid, SpainGoogle Scholar
  5. Bue BD, Stepinski TF (2007) Machine detection of Martian impact craters from digital topography data. IEEE Trans Geosci Remote Sens 45(1):265–274CrossRefGoogle Scholar
  6. Cheng Y, Johnson AE, Matthies LH et al (2003) Optical landmark detection for spacecraft navigation. In: Proceedings of the 13th AAS/AIAA space flight mechanics meeting, Ponce, pp 1785–1803Google Scholar
  7. Cohen JP, Lo HZ, Lu T et al (2016) Crater detection via convolutional neural networks. Proceedings of the lunar and planetary science conference. Lunar and Planetary Inst. Technical Report 47. pp 1143Google Scholar
  8. Craddock RA, Howard AD (2000) Simulated degradation of lunar impact craters and a new method for age dating farside mare deposits. J Geophys Res 105(E8):20387–20401CrossRefGoogle Scholar
  9. Craddock RA, Howard AD (2011) Simulated degradation of lunar impact craters and a new method for age dating local geologic units. In: Lunar and planetary science XXIX, 1423.pdfGoogle Scholar
  10. Emami E, Bebis G, Nefian A et al (2015) Automatic crater detection using convex grouping and convolutional neural networks. Springer, Cham, pp 213–224Google Scholar
  11. Emami E, Bebis G, Nefian A et al (2017) On Crater verification using mislocalized crater regions. IEEE Winter Conf Appl Comput Vis (WACV), Santa Rosa, CA, USA, pp 1098–1104Google Scholar
  12. Flores-Méndez A, Suarez-Cervantes A (2009) Circular degree hough transform. Lect Notes Comput Sci 5856:287–294CrossRefGoogle Scholar
  13. Hao W, Jie J, Guangjun Z (2018) CraterIDNet: an end-to-end fully convolutional neural network for crater detection and identification in remotely sensed planetary images. Remote Sens 10(7):1067CrossRefGoogle Scholar
  14. Hiesinger H, Head JW, Wolf U et al (2010) Ages and stratigraphy of lunar mare basalts in Mare Frigoris and other nearside maria based on crater size-frequency distribution measurements. J Geophys Res 115:E03003CrossRefGoogle Scholar
  15. Jahn H (1994) Crater detection by linear filters representing the Hough Transform. In: ISPRS Commission III symposium: spatial information from digital photogrammetry and computer vision, pp 427–431Google Scholar
  16. Kim JR, Muller J-P (2003) Impact crater detection on optical image and DEM. In: ISPRS WG IV/9: extraterrestrial mapping workshop “Advances in planetary mapping 2003”, Houston, TX, USA, March 22, 2003Google Scholar
  17. Magee M, Chapman CR, Dellenback SW et al (2003) Automated Identification of Martian craters using image processing. In: Lunar and planetary science conference, Houston, USA, March 17–21, 2003Google Scholar
  18. Michael GG (2003) Coordinate registration by automated crater recognition. Planet Space Sci 51(9–10):563–568CrossRefGoogle Scholar
  19. Morota T, Haruyana J (2008) Lunar cratering chronology: statistical fluctuation of crater production frequency and its effect on age determination. Earth, Planets and Space 60(4):265–270CrossRefGoogle Scholar
  20. Neukum G, König B, Arkani-Hamed J (1975) A study of lunar impact crater size-distributions. Earth Moon Planet 12(2):201–229Google Scholar
  21. Neukum G, Ivanov BA, Hartmann WK (2001) Cratering records in the inner solar system in relation to the lunar reference system. Space Sci Rev 96(1-4):55–86CrossRefGoogle Scholar
  22. Plesko CS, Werner SC, Brumby SP, Asphaug E, Neukum G (2006) A statistical analysis of automated crater counts in MOC and HRSC data. In: Proceedings of the 37th Lunar and planetary science conference, p 2012. Abstract (CDROM)Google Scholar
  23. Salamunićcar G, Lončarić S, Mazarico E (2011) LU60645GT and MA132843GT catalogues of Lunar and Martian impact craters developed using a Crater Shape-based interpolation crater detection algorithm for topography data. Planet Space Sci 60(1):236–247CrossRefGoogle Scholar
  24. Sawabe Y, Matsunaga T, Rokugawa S (2006) Automated detection and classification of lunar craters using multiple approaches. Adv Space Res 37(1):21–27CrossRefGoogle Scholar
  25. Shylaja B (2005) Determination of lunar surface ages from crater frequency-size distribution. J Earth Syst Sci 114(6):609–612CrossRefGoogle Scholar
  26. Stepinski TF, Mendenhall MP, Bue BD (2009) Machine cataloging of impact craters on Mars. Icarus 203(1):77–87CrossRefGoogle Scholar
  27. Stöffler D, Ryder G (2001) Stratigraphy and isotope ages of lunar geologic units: chronolgical standard for the inner solar system. Space Sci Rev 96(1-4):9–54CrossRefGoogle Scholar
  28. Urbach ER, Stepinski TF (2009) Automatic detection of sub-km craters in high resolution planetary images. Planet Space Sci 57(7):880–887CrossRefGoogle Scholar
  29. Vijayan S, Vani K, Sanjeevi S (2013) Crater detection, classification and contextual information extraction in lunar images using a novel algorithm. Icarus 226(1):798–815CrossRefGoogle Scholar
  30. Wetzler PG, Honda R, Enke B, Merline WJ, Chapman CR, Burl MC (2005) Learning to detect small impact craters. In: Proceedings of the seventh IEEE workshop on applications of computer vision, pp 178–184Google Scholar
  31. Zuo Wei et al (2016) Contour-based automatic crater recognition using digital elevation models from Chang’E Missions. Comput Geosci 97:79–88CrossRefGoogle Scholar

Copyright information

© Science Press and Institute of Geochemistry, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Lunar and Deep Space Exploration, National Astronomical ObservatoriesChinese Academy of SciencesBeijingChina
  2. 2.School of Astronomy and Space SciencesUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.School of Textile Science and EngineeringXi’an Polytechnic UniversityXi’anChina
  4. 4.College of TextilesDonghua UniversityShanghaiChina

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