Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification

  • Tomasz F. Stepinski
  • Soumya Ghosh
  • Ricardo Vilalta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4265)


Mars probes send back to Earth enormous amount of data. Automating the analysis of this data and its interpretation represents a challenging test of significant benefit to the domain of planetary science. In this study, we propose combining terrain segmentation and classification to interpret Martian topography data and to identify constituent landforms of the Martian landscape. Our approach uses unsupervised segmentation to divide a landscape into a number of spatially extended but topographically homogeneous objects. Each object is assigned a 12 dimensional feature vector consisting of terrain attributes and neighborhood properties. The objects are classified, based on their feature vectors, into predetermined landform classes. We have applied our technique to the Tisia Valles test site on Mars. Support Vector Machines produced the most accurate results (84.6% mean accuracy) in the classification of topographic objects. An immediate application of our algorithm lies in the automatic detection and characterization of craters on Mars.


Support Vector Machine Impact Crater Automatic Recognition Neighborhood Property Terrain Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adams, R., Bischof, L.: Seeded Region Growing. IEEE Trans. Pattern Analysis and Machine Intelligence 16(6), 641–647 (1994)CrossRefGoogle Scholar
  2. 2.
    Baatz, M., Schäpe, A.: Multiresolution Segmentation - An Optimization Approach for High Quality Multi-Scale Image Segmentation. In: Strobl, J., et al. (eds.) Angewandte Geographische Informationsverarbeitung, vol. XII, pp. 12–23. Wichmann, Heidelberg (2000)Google Scholar
  3. 3.
    Barlow, N.G.: Crater Size-Distributions and a Revised Martian Relative Chronology. Icarus 75(2), 285–305 (1988)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Belongie, S., Carson, C., Greenspan, H., Malik, J.: Color and Texture-based Image Segmentation Using EM and its Application to Content-based Image Retrieval. In: Proc. of Sixth IEEE Int. Conf. Comp. Vision, pp. 675–682 (1998)Google Scholar
  5. 5.
    Bentley, J.L.: Multidimensional Binary Search Trees Used for Associative Searching. Communications of ACM 18(9), 509–517 (1975)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)MATHMathSciNetGoogle Scholar
  7. 7.
    Bue, B.D., Stepinski, T.F.: Automated classification of Landforms on Mars. Computers & Geoscience 32(5), 604–614 (2006)CrossRefGoogle Scholar
  8. 8.
    Burl, M.C., Stough, T., Colwell, W., Bierhaus, E.B., Merline, W.J., Chapman, C.: Automated Detection of Craters and Other Geological Features. In: Proc. Int. Symposium on Artificial Intelligence, Robotics and Automation for Space, Montreal, Canada (2001)Google Scholar
  9. 9.
    Chen, Q., Zhou, C., Luo, J., Ming, D.: Fast Segmentation of High-Resolution Satellite Images Using Watershed Transform Combined with an Efficient Region Merging Approach. In: Klette, R., Žunić, J. (eds.) IWCIA 2004. LNCS, vol. 3322, pp. 621–630. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Cintala, M.J., Head, J.W., Mutch, T.A.: Martian Crater Depth/Diameter Relationship: Comparison with the Moon and Mercury. In: Proc. Lunar Sci. Conf., vol. 7, pp. 3375–3587 (1976)Google Scholar
  11. 11.
    Cintala, M.J., Mouginis-Mark, P.J.: Martian Fresh Crater Depth: More Evidence for Subsurface Volatiles. Geophys. Res. Lett. 7, 329–332 (1980)CrossRefGoogle Scholar
  12. 12.
    Cooper, G.F., Herskovits, E.: A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning 9(4), 309–347 (1992)MATHGoogle Scholar
  13. 13.
    Deng, Y., Manjunath, B.S.: Unsupervised Segmentation of Color-Texture Regions in Images and Video. IEEE Trans. Pattern Analysis and Machine Intelligence 23(8), 800–810 (2001)CrossRefGoogle Scholar
  14. 14.
    Honda, R., Iijima, Y., Konishi, O.: Mining of Topographic Feature from Heterogeneous Imagery and Its Application to Lunar Craters. In: Arikawa, S., Shinohara, A. (eds.) Progress in Discovery Science. LNCS, vol. 2281, p. 395. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Jing, F., Li, M.J., Zhang, H.J., Zhang, B.: Unsupervised Image Segmentation Using Local Homogeneity Analysis. In: Proc. IEEE International Symposium on Circuits and Systems, pp. II-456–II-459 (2003)Google Scholar
  16. 16.
    Kim, J.R., Muller, J.-P., van Gasselt, S., Morley, J.G., Neukum, G.: The HRSC Col Team: Automated Crater Detection, A New Tool for Mars Cartography and Chronology. Photogrammetric Engineering & Remote Sensing 71(10), 1205–1217 (2005)Google Scholar
  17. 17.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)Google Scholar
  18. 18.
    Koperski, K., Han, J., Stefanovic, N.: An Efficient two-Step Method for Classification of Spatial Data. In: Proc. Eight Symp. Spatial Data Handling, pp. 45–55 (1998)Google Scholar
  19. 19.
    Molloy, I., Stepinski, T.F.: Automatic Mapping of Valley Networks on Mars. Computers & Geoscience (submitted, 2006)Google Scholar
  20. 20.
    Mouginis-Mark, P.J., Garbeil, H., Boyce, J.M., Ui, C.S.E., Baloga, S.M.: Geometry of Martian Impact Craters: First Results from an Iterative Software Package. J. Geophys. Res. 109, E08996 (2004)Google Scholar
  21. 21.
    Nock, R., Nielsen, F.: Stochastic Region Merging. IEEE Trans. Pattern Analysis and Machine Intelligence 26(11), 1452–1458 (2004)CrossRefGoogle Scholar
  22. 22.
    Rodionova, J.F., Dekchtyareva, K.I., Khramchikhin, A.A., Michael, G.G., Ajukov, S.V., Pugacheva, S.G., Shevchenko, V.V.: Morphological Catalogue of the Craters of Mars. ESA-ESTEC (2000)Google Scholar
  23. 23.
    Smith, D., Neumann, G., Arvidson, R.E., Guinness, E.A., Slavney, S.: Mars Global Surveyor Laser Altimeter Mission Experiment Gridded Data Record. NASA Planetary Data System, MGS-M-MOLA-5-MEGDR-L3-V1.0 (2003)Google Scholar
  24. 24.
    Soderblom, L.A., Condit, C.D., West, R.A., Herman, B.M., Kreidler, T.J.: Martian Planetwide Crater Distributions: Implications for Geologic History and Surface Processes. Icarus 22, 239–263 (1974)CrossRefGoogle Scholar
  25. 25.
    Stepinski, T.F., Vilalta, R.: Digital Topography Models for Martian surfaces. IEEE Geoscience and Remote Sensing Letters 2(3), 260–264 (2005)CrossRefGoogle Scholar
  26. 26.
    Vincent, L., Soille, P.: Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Trans. Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)CrossRefGoogle Scholar
  27. 27.
    Vinogradova, T., Burl, M., Mjosness, E.: Training of a Crater Detection Algorithm for Mars Crater Imagery. In: Aerospace Conference Proc. 2002, pp. 7–3211. IEEE, Los Alamitos (2002)Google Scholar
  28. 28.
    Wetzler, P.G., Enke, B., Merline, W.J., Chapman, C.R., Burl, M.C.: Learning to Detect Small Impact Craters. In: Seventh IEEE Workshops on Computer Vision (WACV/MOTION 2005), vol. 1, pp. 178–184 (2005)Google Scholar
  29. 29.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tomasz F. Stepinski
    • 1
  • Soumya Ghosh
    • 2
  • Ricardo Vilalta
    • 2
  1. 1.Lunar and Planetary InstituteHoustonUSA
  2. 2.Department of Computer ScienceUniversity of HoustonHoustonUSA

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