SAR Image Registration Using Ratio Mutual Information

Article Preview

Abstract:

In order to reduce the noise sensitivity of the SAR (synthetic aperture radar) image registration, a image registration algorithm which basing on the ratio mutual information (RatioMI) is proposed in this paper. Firstly, the ratio images of the reference image and the floating image are gotten by using the ratio operator, and then take the two ratio images as a similar characteristic quantity to construct the similarity measure function which was used in the optimization process of the image registration experiment. The experimental results of the SAR image registration show that the new registration algorithm which based on the RatioMI is effectively in avoiding the local maxima point problems causing by speckle noise.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2630-2637

Citation:

Online since:

December 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] L. G. Brenown: ACM Computing Surveys vol. 24 (1992), pp.325-376.

Google Scholar

[2] B. Zitova, J. Flusser: Image and Vision Computing vol. 21 (2003), pp.977-1000.

Google Scholar

[3] P. Dare, I. Dowman: International Archives of Photogrammetry and Remote Sensing vol. 3 (2000), pp.125-130.

Google Scholar

[4] Y. Bentoutou, N. Taleb, A. Bounoua, K. Kpalma and J. Ronsin: Feature Based Registration of Satellite Images Proc. Of the 2007 15th Intl. Conf. on Digital Signal Processing (DSP2007) (United Kingdom: Wales) pp.419-422.

DOI: 10.1109/icdsp.2007.4288608

Google Scholar

[5] Xiaolong Dai, S. Khorran, in: A Feature-Based Image Registration Algorithm Using Improved Chain-Code Representation Combined with Invariant Moments IEEE Transaction on Geoscience and Remote Sensing 9(1999), 2351-2362.

DOI: 10.1109/36.789634

Google Scholar

[6] H. Xie, L. E. P. Pierce and F. T. Ulaby: Proceedings of IEEE International Symposium on Geoscience and Remote Sensing vol 6(2003), pp.4028-4031.

Google Scholar

[7] Q. Liu, G. H. Wang, J. Zhang and N. Xin, in: Automatic SAR Image Registration Based on Neighborhood Entropy, Synthetic Aperture Radar APSAR 2007 IEEE pp.507-512.

DOI: 10.1109/apsar.2007.4418661

Google Scholar

[8] H. Q. Luong, S Gautama and W. Philips: Geoscience and Remote Sensing Symposium vol. 6(2004), pp.3864-3867.

Google Scholar

[9] T. M. Cover and J. Thomas, in: A Elements of Information Theory, John Wiley & Sons, Inc., (New York), (1991).

Google Scholar

[10] R. Touzi, A. Lopes and P. Bousquet: IEEE Transactions on Geoscience and Remote Sensing , vol. 26(1988), pp.764-73.

DOI: 10.1109/36.7708

Google Scholar

[11] A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens and G. Marchal, in: Automated Multi-modality Image Registration Based on Information Theory, Information Processing in Medical Imaging. Kluwer Academic Publishers pp.263-274. (1995).

DOI: 10.1109/mmbia.1996.534053

Google Scholar

[12] P. Viola, W. M. Wells III. in: Alignment by Maximization of Mutual Information, Proceedings of the fifth International Conference on Computer Vision (MA: Boston) pp.16-23. (1995).

Google Scholar

[13] Jr J. E. Dennis and D. J. Woods, in: Optimization on Microcomputers The Nelder-Mead Simplex Algorithm The ARO Workshop on Microcomputers in Delaware pp.1-7. (1985).

DOI: 10.21236/ada453814

Google Scholar