Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Visual Computing

ISVC 2008: Advances in Visual Computing pp 105–112Cite as

High Resolution Satellite Classification with Graph Cut Algorithms

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5359))

Abstract

In this paper, an unsupervised classification technique is proposed for high resolution satellite imagery. The approach uses graph cuts to improve the k-means algorithm, as graph cuts introduce spatial domain information of the image that is lacking in the k-means. High resolution satellite imagery, IKONOS, and SPOT-5 have been evaluated by the proposed method, showing that graph cuts improve k-means results, which in turn show coherent and continually spatial cluster regions that could be useful for cartographic classification.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lillesand, T.M., Kiefer, R.W., Chipman, J.W.: Remote Sensing and Image Interpretation. Wiley, Chichester (2003)

    Google Scholar 

  2. Atkinson, P.M.: Resolution manipulation and sub-pixel mapping. In: de Jong, S.M., van der Meer, F.D. (eds.) Remote Sensing Image Analysis: Including the Spatial Domain (Remote Sensing and Digital Image Processing), pp. 51–70. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  3. Berberoglu, S., Curran, P.J.: Merging Spectral and Textural Information for Classifying Remotely Sensed Images. In: de Jong, S.M., van der Meer, F.D. (eds.) Remote Sensing Image Analysis: Including the Spatial Domain (Remote Sensing and Digital Image Processing), pp. 113–135. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  4. van der Werff, H., Lucieer, A.: A contextual algorithm for detection of mineral alteration Halos with hyperspectral remote sensing. In: de Jong, S.M., van der Meer, F.D. (eds.) Remote Sensing Image Analysis: Including the Spatial Domain (Remote Sensing and Digital Image Processing), pp. 201–210. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Carvalbo, L.M., Acerbi, F.W., Clevers Jr., J., Fonseca, L., de Jong, S.M.: Multiscale feature extraction from images using wavelets. In: Steven, M., de Jong, S.M., van der Meer, F.D. (eds.) Remote Sensing Image Analysis: Including the Spatial Domain (Remote Sensing and Digital Image Processing), pp. 237–269. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Boykov, Y., Funka-Lea, G.: Graph Cuts and Efficient N-D Image Segmentation. International Journal of Computer Vision 70(2), 109–131 (2006)

    Article  Google Scholar 

  7. Boykov, Y., Kolmogorov, V.: An Experimental Comparison of Min-Cut/Max-Flow Algorithmsfor Energy Minimization in Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1124–1137 (2004)

    Article  Google Scholar 

  8. Kolmogorov, K., Zabih, Z.: What Energy Functions can be Minimized via Graph Cuts? IEEE Transactions on Pattern Analysis and Machine Intellegence 26(2), 147–159 (2004)

    Article  MATH  Google Scholar 

  9. Elias, P., Feinstein, A., Shannon, C.E.: A note on the maximum flow through a network. IRS Transactions on Information Theory 2, 117–119 (1956)

    Article  Google Scholar 

  10. Ford Jr., L.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press, Princeton (1962)

    MATH  Google Scholar 

  11. Greig, D., Porteous, B., Seheult, A.: Exact Maximum A Posteriori Estimation for Binary Images, J. Royal Statistical Soc., Series B 51(2), 271–279 (1989)

    Google Scholar 

  12. Lin, M.H.: Surfaces with Occlusions from Layered Stereo. Int’l J. Computer Vision 1(2), 1–15 (1999)

    Google Scholar 

  13. Kolmogorov, V., Zabih, R.: Multi-Camera Scene Reconstruction via Graph Cuts. In: Proc. European Conf. Computer Vision, vol. 3, pp. 82–96 (2002)

    Google Scholar 

  14. Boykov, Y., Jolly, M.-P.: Interactive Organ Segmentation Using Graph Cuts. In: Proc. Medical Image Computing and Computer-Assisted Intervention, pp. 276–286 (2000)

    Google Scholar 

  15. Goldberg, A.V., Tarjan, R.E.: A new approach to the maximum-flow problem. Journal of the ACM (JACM) 35(4), 921–940 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  16. Boykov, Y., Veksler, O., Zabih, R.: Markov random fields with efficient approximations. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 648–655 (1998)

    Google Scholar 

  17. The Weizmann Institute of Science Faculty of Mathematics and Computer Science Computer Vision Lab, http://www.wisdom.weizmann.ac.il/~bagon/matlab.html

  18. Richard, D.O., Hart, E., Peter y Stark David, G.: Pattern Classification. Wiley-Interscience, Hoboken (2004)

    Google Scholar 

  19. del-Toro-Almenares, A., Mihai, C., Vanhamel, I., Sahli, H.: Graph Cuts Approach to MRF Based Linear Feature Extraction in Satellite Images. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 162–171. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mathematics Department, School of Geodesy and Cartography, Alcalá University, 28871, Madrid, Spain

    Adrian A. López & José A. Malpica

Authors
  1. Adrian A. López
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José A. Malpica
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

López, A.A., Malpica, J.A. (2008). High Resolution Satellite Classification with Graph Cut Algorithms. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89646-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89645-6

  • Online ISBN: 978-3-540-89646-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation