A Rigid Point Set Registration of Remote Sensing Images Based on Genetic Algorithms and Hausdorff Distance

Authors: F. Meskine, N. Taleb, M. Chikr El-Mezouar, K. Kpalma, A. Almhdie

Abstract:

Image registration is the process of establishing point by point correspondence between images obtained from a same scene. This process is very useful in remote sensing, medicine, cartography, computer vision, etc. Then, the task of registration is to place the data into a common reference frame by estimating the transformations between the data sets. In this work, we develop a rigid point registration method based on the application of genetic algorithms and Hausdorff distance. First, we extract the feature points from both images based on the algorithm of global and local curvature corner. After refining the feature points, we use Hausdorff distance as similarity measure between the two data sets and for optimizing the search space we use genetic algorithms to achieve high computation speed for its inertial parallel. The results show the efficiency of this method for registration of satellite images.

Keywords: Feature extraction, Genetic algorithms, Hausdorff distance, Image registration, Point registration.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1086291

References:

[1] B. Zitova, J. Flusser, "Image registration methods: a survey". Image and Vision Computing, vol. 21, no. 11, pp. 977-1000, 2003.
[2] F. Meskine, M. Chikr EL Mezouar, N. Taleb, "A Rigid image registration based on the nonsubsampled contourlet transform and genetic algorithms". Sensors journal, vol.10(9), pp. 8553-8571, 2010.
[3] Y. Bentoutou, N. Taleb, K. Kpalma, J. Ronsin, "An automatic image registration for applications in remote sensing", IEEE Transactions on Geoscience and Remote Sensing, vol.(43), pp. 2127-2137, 2005.
[4] P.J Besl, N.D McKay, "A method for registration of 3D shapes", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.n┬░14 (2), pp.239- 256, 1992.
[5] Fitzgibbon AW. Robust registration of 2D and 3D point sets. BMVC2001; 2001.
[6] Granger S, Pennec X. Multi-scale EM-ICP. A fast and robust approach for surface registration. ECCV2002(4), pp. 418-432. 2002.
[7] Daniel P. Huttenlocher, Gregory. Klanderman, and William Rucklidge. Comparing images using the Hausdorff distance. IEEE transactions on Pattern Analysis and Machine Intelligence, Vol.15(9), pp. 850-863, 1993.
[8] D.E.Goldberg "Genetic Algorithm in search, optimization and machine learning, 1989, Addison Wesley.
[9] J. Jacq, C. Roux, Registration of 3D images by genetic optimization. PatternRecognition Letters 16, pp. 823-841, 1995.
[10] K. Brunnstr¨om, A. Stoddart, "Genetic algorithms for free-form surface matching", in: Proc. 13th International Conference on Pattern Recognition, Vol. 4, 1996, pp. 689-693.
[11] Stamos, I., Leordeanu, M. Automated feature-based range registration of urban scenes of large scale. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Madison, June 16- 22, vol. II: 555- 561, 2003.
[12] Xiao Chen He, Nelson H. C. Yung. Corner detector based on global and local curvature properties. Optical Engineering, vol 47(5), 057008, May 2008.
[13] Xiaoming Peng, Wufan Chen, Qian Ma,. Feature-based nonrigid image registration using a Hausdorff distance matching measure. Optical Engineering 46(5), 057201, May 2007.
[14] M.-P. Dubuisson, A.K. Jain. A modified Hausdorff distance for object matching. Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol (1), pp. 566-568, October 1994.
[15] F. Meskine, N.Taleb, Ahmad Almhdie-Imjabber, "A 2D Rigid Point Registration for Satellite Imaging Using Genetic Algorithms", Lecture Notes on Computer Science LNCS 7340 Springer, pp. 442-450, in the proceedings of The 5th International Conference on Image and Signal Processing ICISP 2012, June 28 - 30, Agadir, Morocco, 2012.