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Remote Sensing Image Registration Based on Particle Swarm Optimization and Mutual Information

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Information Systems Design and Intelligent Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 340))

Abstract

The image registration is an indispensable process in remote sensing image processing. The remote sensing registration data is the process of aligning one image to a second image of the same scene that is acquired at the same or at different times by the different or the same sensors. This paper proposes an optimization approach for remote sensing image registration. The approach is proposed for determining pairs of corresponding points between the images, the approach based on the implementation of particle swarm optimization (PSO) used as a function optimizer and mutual information (MI) is used as a similarity measure. The first, Landmarks were chosen manually and used thin plate spline (TPS) to provide a geometric representation for the relative locations of corresponding landmarks. Secondly, MI was used as a cost function to determine the degree of similarity between two images. Finally, PSO was used to improve the correspondence between the landmarks and to maximize MI function.

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References

  1. Qiao, Y., Lelieveldt, B.P.F., Staring, M.: Fast automatic estimation of the optimization step size for nonrigid image registration. SPIE Medical Imaging, International Society for Optics and Photonics (2014)

    Google Scholar 

  2. Bouchihaan, R., Besbes, K.: Automatic remote sensing image registration using SURF. Int. J. Comput. Theor. Eng. 5(1), 88–92 (2013)

    Article  Google Scholar 

  3. Ye, Y., Shan, J.: A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences. ISPRS J. Photogrammetry Remote Sens. 90, 83–95 (2014)

    Article  Google Scholar 

  4. Wang, L., Niu, Z., Wu, C., Xie, R., Huang, H.: A robust multisource image automatic registration system based on the SIFT descriptor. Int. J. Remote Sens. 33(12), 3850–3869 (2012)

    Article  Google Scholar 

  5. Hong, G., Zhang, Y.: Wavelet-based image registration technique for high-resolution remote sensing images. Comput. Geosci. 34(12), 1708–1720 (2008)

    Article  Google Scholar 

  6. Chen, H., Arora, M., Varshney, P.K.: Mutual information-based image registration for remote sensing data. Int. J. Remote Sens. 24(18), 3701–3706 (2003)

    Article  Google Scholar 

  7. Li, Z., Bao, Z., Li, H., Liao, G.: Image autocoregistration and InSAR interferogram estimation using joint subspace projection. IEEE Trans. Geosci. Remote Sens. 44(2), 288–297 (2006)

    Article  Google Scholar 

  8. Orchard, J.: Efficient least squares multimodal registration with a globally exhaustive alignment search. IEEE Trans. Image Process. 16(10), 2526–2534 (2007)

    Article  MathSciNet  Google Scholar 

  9. Wong, A., Orchard, J.: Efficient FFT-accelerated approach to invariant optical-lidar registration. IEEE Trans. Geosci. Remote Sens. 46(11), 3917–3925 (2008)

    Article  Google Scholar 

  10. Zavorin, I., Le Moigne, J.: Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery. IEEE Trans. Image Process. 14(6), 770–782 (2005)

    Article  Google Scholar 

  11. Liu, J.G., Yan, H.: Phase correlation pixel to pixel image coregistration based on optical flow and median shift propagation. Int. J. Remote Sens. 29(20), 5943–5956 (2008)

    Article  Google Scholar 

  12. Liu, X., Tian, Z., Chai, C., Fu, H.: Multiscale registration of remote sensing image using robust SIFT features in Steerable-Domain. Egypt. J. Remote Sens. Space Sci. 14(2), 63–72 (2011)

    Google Scholar 

  13. Wahed, M., El-tawel, G.S., El-karim, A.G.: Automatic image registration technique of remote sensing images. Int. J. Adv. Comput. Sci. Appl. 4(2), 177–187 (2013)

    Google Scholar 

  14. Cai, G.R., Jodoin, P.M., Li, S.Z., Wu, Y.D., Su, S.Z., Huang, Z.K.: Perspective-SIFT: an efficient tool for low-altitude remote sensing image registration. Sig. Process. 93(11), 3088–3110 (2013)

    Article  Google Scholar 

  15. Goshtasby, A.: Registration of image with geometric distortion,”. IEEE Trans. Geosci. Remote Sens. 26(1), 60–64 (1988)

    Article  Google Scholar 

  16. Bookstein, F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Patt. Anal. Mach. Intell. 11(6), 567–585 (1989)

    Article  MATH  Google Scholar 

  17. Ahmed, S.A., Ghali, N.I.: Optimize the correspondence using particle swarm optimization for medical image registration. In: IEEE 12th International Conference on Hybrid Intelligent Systems (HIS), pp. 80–84 (2012)

    Google Scholar 

  18. Mitra, J. Oliver, A., Marti, R., Llado, X., Vilanova, J. and Meriaudeau, F.: A thin-plate spline based multimodal prostate registration with optimal correspondences. In: Proceedings of International Conference on Digital Image Computing, Techniques and Applications, DICTA, Sydney, Australia, pp. 7–11 (2010)

    Google Scholar 

  19. Xiao, K., Ho, S.H., Hassanien, A.E.: Brain magnetic resonance image lateral ventricles deformation analysis and tumor prediction. Malays. J. Comput. Sci. 20(2), 115–132 (2007)

    Google Scholar 

  20. Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. Int. J. Comput. Vision 24(2), 137–154 (1997)

    Article  Google Scholar 

  21. Wirth, M.A., Narhan, J., Gray, D.W.: Nonrigid mammogram registration using mutual information. In: Medical Imaging 2002, pp. 562–573. International Society for Optics and Photonics (2002)

    Google Scholar 

  22. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4(2), pp. 1942–1948 (1995)

    Google Scholar 

  23. Shi, Y.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks Society, vol. 4(13), pp. 1942–1948 (2004)

    Google Scholar 

  24. Zhan, Z.H., Zhang, J., Li, Y., Shi, Y.H.: Orthogonal learning particle swarm optimization. IEEE Trans. Evol. Comput. 15(6), 832–847 (2011)

    Article  Google Scholar 

  25. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence. The IEEE International Conference, pp. 69–73 (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Nuclear Materials Authority, Cairo, Egypt

    Reham Gharbia

  2. Faculty of Science, Al-Azhar University, Cairo, Egypt

    Sara A. Ahmed

  3. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul ella Hassanien

  4. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Reham Gharbia, Sara A. Ahmed & Aboul ella Hassanien

Authors
  1. Reham Gharbia
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  2. Sara A. Ahmed
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  3. Aboul ella Hassanien
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Corresponding author

Correspondence to Reham Gharbia .

Editor information

Editors and Affiliations

  1. University of Kalyanai, Kalyanai, West Bengal, India

    J. K. Mandal

  2. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  3. Dean, Faculty of Engineering, Technology, University of Kalyani, Kalyani, West Bengal, India

    Manas Kumar Sanyal

  4. Engineering and Technological Studies, University of Kalyani, Kalyani, West Bengal, India

    Partha Pratim Sarkar

  5. Department Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Anirban Mukhopadhyay

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Gharbia, R., Ahmed, S.A., Hassanien, A.e. (2015). Remote Sensing Image Registration Based on Particle Swarm Optimization and Mutual Information. In: Mandal, J., Satapathy, S., Kumar Sanyal, M., Sarkar, P., Mukhopadhyay, A. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 340. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2247-7_41

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  • DOI: https://doi.org/10.1007/978-81-322-2247-7_41

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2246-0

  • Online ISBN: 978-81-322-2247-7

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