The selection of matching method is critical to the scene matching navigation system, as it determines the accuracy of navigation. A coarse-to-fine matching method, which combines the area-based and feature-based matching method, is presented to meet the requirements of navigation, including the real-time performance, the sub-pixel accuracy and the robustness. In the coarse matching stage, the real-time performance is achieved by a pyramid multi-resolution technique, and the robustness is improved by multi-scale circular template fusion. In the precise matching stage, an improved SIFT method is introduced to calculate the matching position and the rotation angle. To validate the method, some experiments are completed. The results show that the proposed method can achieve the sub-pixel matching accuracy and improve the angle accuracy to 0.1°.
2. Inglada, J. and F. Adragna, "Automatic multi-sensor image registration by edge matching using genetic algorithms," IEEE Int. Geosci. Remote Sens. Symposium, Vol. 5, 2313-2315, Sydney, NSW, Australia, 2001.
3. Liu, H. Z., B. L. Guo, and Z. Z. Feng, "Pseudo-log-polar Fourier transform for image registration," IEEE Signal Processing Letters, Vol. 13, No. 1, 17-20, 2006.
4. You, J. and P. A. Bhattacharya, "Wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment," IEEE Trans. Image Processing, Vol. 9, No. 9, 1547-1559, 2000.
5. Kusiek, A. and J. Mazur, "Analysis of scattering from arbitrary configuration of cylindrical objects using hybrid finite-difference mode-matching method," Progress In Electromagnetics Research, Vol. 97, 105-127, 2009.
6. Riabi, M. L., R. Thabet, and M. Belmeguenai, "Rigorous design and efficient optimization of quarter-wave transformers in metallic circular waveguides using the mode-matching method and the genetic algorithm," Progress In Electromagnetics Research, Vol. 68, 15-33, 2007.
7. Ren, S., W. Chang, and X. Liu, "SAR image matching method based on improved SIFT for navigation system," Progress In Electormagnetics Research M, Vol. 18, 259-269, 2011.
8. Lowe, D. G., "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, Vol. 60, No. 2, 91-110, 2004.
9. Yang, Y., et al., "A high-precision localization algorithm by improved SIFT key-points," 2nd Int. Congress on Image and Signal Processing, 1-6, Tianjin, 2009.
10. Mikolajczyk, K. and C. Schmid, "A performance evaluation of local descriptors," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 27, No. 10, 1615-1630, 2005.
11. Ehlers, M., "Multi-sensor image fusion techniques in remote sensing," ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 46, No. 1, 19-30, 1991.
12. Dai, X. L. and S. Khorram, "A feature-based image registration algorithm using improved chain- code representation combined with invariant moments," IEEE Trans. Geosci. Remote Sens., Vol. 37, No. 5, 2351-2362, 2002.
13. Zhang, Y. C., P. Wang, and G. Y. Zhang, "An information fusion approach and its application based on D-S evidence theory and neural network," 27th Chinese Control Conference Proceedings, 623-626, Kunming, 2008.
14. Grabner, M., H. Grabner, and H. Bischof, "Fast approximated SIFT," Asian Conferonce on Comput. Vis., 918-927, Hyderabad, India, 2006.
15. Ke, Y. and R. Sukthankar, "PCA-SIFT: A more distinctive representation for local image descriptors," Proceedings of IEEE Comput. Soci. Conference on Comput. Vis. Pattern. Recog., 506-513, Washington DC, USA, 2004.
16. Yi, Z., Z. G. Cao, and X. Yang, "Multi-spectral remote image registration based on SIFT," Electronics Letters, Vol. 44, No. 2, 107-108, 2008.
17. Bastanlar, Y., A. Temizel, and Y. Yardimci, "Improved SIFT matching for image pairs with scale di®erence," Electronics Letters, Vol. 46, No. 5, 346-348, 2010.
18. Xie, H., L. Pierce, and F. Ulaby, "Statistical properties of logarithmically transformed speckle," IEEE Trans. Geosci. Remote Sens., Vol. 40, No. 3, 721-727, 2002.
19. He, F. F., J. Y. Sun, and W. P. Guo, "A practical method for evaluating capability of scene matching algorithms," 7th Int. Conf. Computer-Aided Industrial Design and Conceptual Design, 1-6, Hangzhou, 2006.
20. Shi, H. L. and B. Hu, Image registration using a new scheme of wavelet decomposition, IEEE Instrumentation and Measurement Technology Conference, 235-239, Victoria, 2008.
21. Li, D. and Y. H. Zhang, Geometric feature-based image co-registration approach for InSAR, 2nd Asian-Paci¯c Conf. Synthetic Aperture Radar Proceedings, 1026-1030, Xi'an, 2009.