A Modified SIFT Descriptor for Image Matching under Spectral Variations

  • Sajid Saleem
  • Robert Sablatnig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


In multispectral imaging multiple discrete wavelength bands are used to image a scene. The imaging process maps the scene contents to different intensity levels and varies the scene appearance from band to band. This induces intensity variations among the spectral images and effects the performance of SIFT for cross spectral image matching. This paper proposes modifications to the SIFT descriptor in order to improve its robustness against spectral variations. The proposed modifications are based on fact, that edges remain well preserved in multispectral imaging and we can achieve better image matching results by boosting the contribution of local edges in the SIFT descriptor construction process. Therefore, we propose a Local Contrast (Δ) and a Differential Excitation (ξ) function for the construction of SIFT descriptors. The experimental results show, that the performance of Δ-SIFT and ξ-SIFT is superior to standard SIFT for image matching under spectral variations.


SIFT spectral images interest regions image matching 


  1. 1.
    Brown, M., Su, S.: Multi-spectral SIFT for scene category recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 177–184 (2011)Google Scholar
  2. 2.
    Chakrabarti, A., Zickler, T.: Statistics of Real-World Hyperspectral Images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 193–200 (2011)Google Scholar
  3. 3.
    Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: WLD: a robust local image descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1705–1720 (2010)CrossRefGoogle Scholar
  4. 4.
    Hasan, M., Jia, X., Robles-Kelly, A., Zhou, J., Pickering, M.R.: Multi-spectral remote sensing image registration via spatial relationship analysis on SIFT keypoints. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 1011–1014 (2010)Google Scholar
  5. 5.
    Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 511–517 (2004)Google Scholar
  6. 6.
    Leykin, A., Hammoud, R.: Pedestrian tracking by fusion of thermal-visible surveillance videos. Machine Vision and Applications 21(4), 587–595 (2010)CrossRefGoogle Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  9. 9.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 65(1), 43–72 (2005)CrossRefGoogle Scholar
  10. 10.
    Saleem, S., Bais, A., Sablatnig, R.: A performance evaluation of SIFT and SURF for multispectral image matching. In: International Conference on Image Analysis and Recognition, pp. 166–173 (2012)Google Scholar
  11. 11.
    Van Herk, M.: A fast algorithm for local minimum and maximum filters on rectangular and octagonal kernels. Pattern Recognition Letters 13(7), 517–521 (1992)CrossRefGoogle Scholar
  12. 12.
    Vural, M., Yardimci, Y., Temizel, A.: Registration of multispectral satellite images with orientation-restricted SIFT. IEEE International Geoscience and Remote Sensing Symposium 3, 243–246 (2009)Google Scholar
  13. 13.
    Yi, Z., Zhiguo, C., Yang, X.: Multi-spectral remote image registration based on SIFT. Electronics Letters 44(2), 107–108 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sajid Saleem
    • 1
  • Robert Sablatnig
    • 1
  1. 1.Computer Vision Lab, Institute of Computer Aided AutomationVienna University of TechnologyViennaAustria

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