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Journal of Sensors
Volume 2016, Article ID 3789570, 9 pages
http://dx.doi.org/10.1155/2016/3789570
Research Article

A Registration Scheme for Multispectral Systems Using Phase Correlation and Scale Invariant Feature Matching

1Key Laboratory of 3D Information Acquisition and Application of Ministry of Education, Capital Normal University, Beijing 100048, China
2School of Mechanical Engineering & Automation, Beihang University, Beijing 100083, China

Received 3 July 2015; Revised 20 November 2015; Accepted 24 November 2015

Academic Editor: Hairong Qi

Copyright © 2016 Hanlun Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. Oppelt and W. Mauser, “Airborne visible/infrared imaging spectrometer AVIS: design, characterization and calibration,” Sensors, vol. 7, no. 9, pp. 1934–1953, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. P. V. Gorsevski and P. E. Gessler, “The design and the development of a hyperspectral and multispectral airborne mapping system,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 64, no. 2, pp. 184–192, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Yang, J. H. Everitt, M. R. Davis, and C. Mao, “A CCD camera-based hyperspectral imaging system for stationary and airborne applications,” Geocarto International, vol. 18, no. 2, pp. 71–80, 2003. View at Publisher · View at Google Scholar
  4. J. Wu, Y. S. Cao, H. S. Yang, and Z. G. Liu, “Automatic registration of high-resolution multispectral imageries released by low-altitude unmanned airship,” Journal of Remote Sensing, vol. 16, no. 3, pp. 625–643, 2012. View at Google Scholar
  5. B. Zitová and J. Flusser, “Image registration methods: a survey,” Image and Vision Computing, vol. 21, no. 11, pp. 977–1000, 2003. View at Publisher · View at Google Scholar · View at Scopus
  6. L. G. Brown, “A survey of image registration techniques,” ACM Computing Surveys, vol. 24, no. 4, pp. 325–376, 1992. View at Publisher · View at Google Scholar · View at Scopus
  7. L. M. G. Fonseca and B. S. Manjunath, “Registration techniques for multisensor remotely sensed imagery,” Photogrammetric Engineering and Remote Sensing, vol. 62, no. 9, pp. 1049–1056, 1996. View at Google Scholar · View at Scopus
  8. A. Goshtasby, 2-D and 3-D Image Registration for Medical, Remote Sensing and Industrial Applications, Wiley-Interscience, New York, NY, USA, 2005.
  9. J. Li, Q.-M. Peng, and Z.-H. Fan, “A survey of sub-pixel image registration methods,” Journal of Image and Graphics, vol. 13, no. 11, pp. 2070–2075, 2008. View at Google Scholar
  10. H. Foroosh, J. B. Zerubia, and M. Berthod, “Extension of phase correlation to subpixel registration,” IEEE Transactions on Image Processing, vol. 11, no. 3, pp. 188–200, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. H. S. Stone, M. T. Orchard, E.-C. Chang, and S. A. Martucci, “A fast direct Fourier-based algorithm for subpixel registration of images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 10, pp. 2235–2243, 2001. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Sedaghat, M. Mokhtarzade, and H. Ebadi, “Uniform robust scale-invariant feature matching for optical remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4516–4527, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Gruen, “Adaptive least squares correlation: a powerful image matching technique,” South African Journal of Photogrammetry, Remote Sensing and Cartography, vol. 14, no. 3, pp. 175–187, 1985. View at Google Scholar
  14. W. Li and H. Leung, “A maximum likelihood approach for image registration using control point and intensity,” IEEE Transactions on Image Processing, vol. 13, no. 8, pp. 1115–1127, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. Y. He, A. B. Hamza, and H. Krim, “A generalized divergence measure for robust image registration,” IEEE Transactions on Signal Processing, vol. 51, no. 5, pp. 1211–1220, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. H.-M. Chen, M. K. Arora, and P. K. Varshney, “Mutual information-based image registration for remote sensing data,” International Journal of Remote Sensing, vol. 24, no. 18, pp. 3701–3706, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. J. P. Kern and M. S. Pattichis, “Robust multispectral image registration using mutual-information models,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1494–1505, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Goshtasby, S. H. Gage, and J. F. Bartholic, “A two-stage cross correlation approach to template matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 3, pp. 374–378, 1984. View at Google Scholar · View at Scopus
  19. Y. Keller and A. Averbuch, “Multisensor image registration via implicit similarity,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 794–801, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Juan and O. Gwun, “A comparison of SIFT, PCA-SIFT and SURF,” International Journal of Image Processing, vol. 3, no. 4, pp. 143–152, 2009. View at Google Scholar
  21. K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615–1630, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Hast, J. Nysjö, and A. Marchetti, “Optimal RANSAC—towards a repeatable algorithm for finding the optimal set,” Journal of WSCG, vol. 21, no. 1, pp. 21–30, 2013. View at Google Scholar · View at Scopus
  24. H. Gonçalves, L. Corte-Real, and J. A. Goncalves, “Automatic image registration through image segmentation and SIFT,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 7, pp. 2589–2600, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Hasan, X. Jia, A. Robles-Kelly, J. Zhou, and M. R. Pickering, “Multi-spectral remote sensing image registration via spatial relationship analysis on sift keypoints,” in Proceedings of the 30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS '10), pp. 1011–1014, IEEE, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Gong, S. Zhao, L. Jiao, D. Tian, and S. Wang, “A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 7, pp. 4328–4338, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, Cambridge, UK, 2nd edition, 2003. View at MathSciNet
  28. G. A. Thomas, “Television motion measurement for DATV and other applications,” NASA STI/Recon Technical Report N 88: 13496, 1987. View at Google Scholar
  29. I. E. Abdou, “Practical approach to the registration of multiple frames of video images,” in Visual Communications and Image Processing '99, vol. 3653 of Proceedings of SPIE, pp. 371–382, International Society for Optics and Photonics, San Jose, Calif, USA, January 1998. View at Publisher · View at Google Scholar
  30. W. S. Hoge, “A subspace identification extension to the phase correlation method [MRI application],” IEEE Transactions on Medical Imaging, vol. 22, no. 2, pp. 277–280, 2003. View at Publisher · View at Google Scholar
  31. D. Keren, S. Peleg, and R. Brada, “Image sequence enhancement using sub-pixel displacement,” in Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition, pp. 742–746, June 1988. View at Scopus
  32. P. Vandewalle, S. Süsstrunk, and M. Vetterll, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP Journal on Applied Signal Processing, vol. 2006, Article ID 071459, pp. 1–14, 2006. View at Publisher · View at Google Scholar · View at Scopus