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A Performance Evaluation of SIFT and SURF for Multispectral Image Matching

  • Sajid Saleem
  • Abdul Bais
  • Robert Sablatnig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7324)

Abstract

This paper evaluates the performance of SIFT and SURF for cross band matching of multispectral images. The evaluation is based on matching a reference spectral image with the images acquired at different spectral bands. The reference image possesses scale and (in-plane) rotational differences in addition to spectral variations. Additive white Gaussian noise is also added to compare performance degradation at different noise levels. We use the precision and repeatability criteria for performance evaluation. Experimental results demonstrate that SIFT performs better than SURF in multispectral environment.

Keywords

SIFT SURF multispectral images cross band image matching 

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References

  1. 1.
  2. 2.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Denman, S., Lamb, T., Fookes, C., Chandran, V., Sridharan, S.: Multi-spectral fusion for surveillance system. Journal of Computers and Electrical Engineering 36(4), 643–663 (2010)zbMATHCrossRefGoogle Scholar
  4. 4.
    Diem, M., Lettner, M., Sablatnig, R.: Multi-spectral image acquisition and registration of ancient manuscripts. In: Proceeding of 31st AAPR/OAGM Workshop, vol. 224, pp. 129–136 (2007)Google Scholar
  5. 5.
    Fischler, M.A., Bolles, R.C.: Random sample concensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Guoa, L., Chehataa, N., Malletb, C., Boukira, S.: Relevance of airborne lidar and multispectral image data for urban scene classification using random forests. Journal of Photogrammetry and Remote Sensing 66(1), 56–66 (2011)CrossRefGoogle Scholar
  7. 7.
    Juan, L., Gwon, O.: Comparison of sift, pcasift and surf. International Journal of Image Processing 3(4), 143–152 (2009)Google Scholar
  8. 8.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal on Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  9. 9.
    Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: International Conference on Computer Vision, vol. 1, pp. 525–531 (2001)Google Scholar
  10. 10.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 65, 43–72 (2005)CrossRefGoogle Scholar
  11. 11.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis & Machine Intelligence 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  12. 12.
    Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal on Computer Vision 37(2), 151–172 (2000)zbMATHCrossRefGoogle Scholar
  13. 13.
    Stollnitz, E.J., DeRose, T.D., Salesin, D.H.: Wavelets for computer graphics:a primer, part i. IEEE ComputerGraphics and Applications 15(3), 76–84 (1995)CrossRefGoogle Scholar
  14. 14.
    Switonski, A., Janik, L., Jedrasiak, K.: Individual features of the skin spectra. In: Proceedings of the World Congress on Engineering and Computer Science, vol. 1, pp. 147–151 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sajid Saleem
    • 1
  • Abdul Bais
    • 2
  • Robert Sablatnig
    • 1
  1. 1.Computer Vision LabInstitute of Computer Aided Automation, Vienna University of TechnologyViennaAustria
  2. 2.Faculty of Engineering and Applied ScienceUniversity of ReginaReginaCanada

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