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

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.

Authors:

• F. Meskine
• N. Taleb
• M. Chikr El-Mezouar
• K. Kpalma
• A. Almhdie

Keywords:

• Feature extraction
• Genetic algorithms
• Hausdorff distance
• Image registration
• Point registration.

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.