Abstract
Image registration is a classical problem that addresses the problem of finding a geometric transformation that best aligns two images. Since the amount of multisensor remote sensing imagery are growing tremendously, the search for matching transformation with mutual information is very time-consuming and tedious, and fast and automatic registration of images from different sensors has become critical in the remote sensing framework. So the implementation of automatic mutual information based image registration methods on high performance machines needs to be investigated. First, this paper presents a parallel implementation of a mutual information based image registration algorithm. It takes advantage of cluster machines by partitioning of data depending on the algorithm’s peculiarity. Then, the evaluation of the parallel registration method has been presented in theory and in experiments and shows that the parallel algorithm has good parallel performance and scalability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brown, L.G.: A survey of image registration techniques. ACM Comput. Surv. 24(4), 325–376 (1992)
Townshend, J.R.G., Justice, C.O., Gurney, C., McManus, J.: The impact of misregistration on change detection. IEEE Trans. Geosci. and Remote Sensing 30, 1054–1060 (1992)
Khorram, X.D.S.: A hierarchical methodology framework for multisource data fusion in vegetation classification. Int. J. Remote Sens. 19(18), 3697–3701 (1998)
Antoine Maintz, J.B., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)
Kafatos, M., Bergman, L., Chinman, R., El-Ghazawi, T., Nittel, S., Olsen, L., Wang, X.S.: Data exchanges and interoperability in distributed Earth science information systems. In: Proceedings of 11th International Conference on Scientific and Statistical Database Management, Cleveland, Ohio, USA (1999)
Chalermwat, P., El-Ghazawi, T., LeMoigne, J.: GA-based Parallel Image Registration on Parallel Clusters. In: IPPS/SPDP Workshops (1999)
Le Moigne, J., Campbell, W.J., Cromp, R.F.: An automated Parallel Image Registration Technique Based on the Correlation of Wavelet Features. IEEE Trans. Geosci. and Remote Sensing 40(8), 1849–1864 (2002)
Zhou, H., Yang, X., Liu, H., Tang, Y.: First Evaluation of Parallel Methods of Automatic Global Image Registration Based on Wavelets. In: The International Conference on Parallel Processing, Oslo, Norway, pp. 129–136 (2005)
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality Image Registration by Maximization of Mutual Information. IEEE Trans. Medical imaging 16(2), 187–198 (1997)
Wells III, W.M., Viola, P., Kikinis, R.: Multi-modal Volume Registration by Maximization of Mutual Information. In: Medical Robotics and Computer Assisted Surgery, pp. 55–62. John Wiley & Sons, New York (1995)
Pluim, J.P.W., Antoine Maintz, J.B., Viergever, M.A.: Mutual Information Based Registration of Medical Images: a Survey. IEEE Trans. Medical Imaging 22(8), 986–1004 (2003)
Johnson, K., Cole-Rhodes, A., Zavorin, I., Le Moigne, J.: Mutual information as a similarity measure for remote sensing image registration. In: Proc. SPIE Aerosense 2001, Geo-Spatial Image and Data Exploitation II, Orlando, FL, vol. 4383, pp. 51–61 (2001)
Chen, H.-M., Varshney, P.K., Arora, M.K.: Performance of Mutual Information Similarity Measure for Registration of Multitemporal Remote Sensing Images. IEEE Trans. Geosci. and Remote Sensing 41(11), 2445–2454 (2003)
Cole-Rhodes, A.A., Johnson, K.L., LeMoigne, J., Zavorin, I.: Multiresolution Registration of Remote Sensing Imagery by Optimization of Mutual Information Using a Stochastic Gradient. IEEE Trans. Image Processing 12(12), 1495–1511 (2003)
Maes, F., Vandermeulen, D., Suetens, P.: Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information. Medical Image Analysis 3(4), 373–386 (1999)
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C. Cambridge University Press, Cambridge (1992)
Alexandrov, A., Ionescu, M., Schauser, K.E., Scheiman, C.: LogGP: incorporating long messages into the LogP model - one step closer towards a realistic model of parallel computation. In: Procs. of the 7th Annual ACM Symp. on Parallel Algorithms and Architectures, pp. 95–105 (1995)
Amdahl, G.M.: Validity of the single-processor approach to achieving large scale computing capabilities. In: Proc. AFIPS Conf., Reston, VA, vol. 30, pp. 483–485 (1967)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Du, Y., Zhou, H., Wang, P., Yang, X., Liu, H. (2006). A Parallel Mutual Information Based Image Registration Algorithm for Applications in Remote Sensing. In: Guo, M., Yang, L.T., Di Martino, B., Zima, H.P., Dongarra, J., Tang, F. (eds) Parallel and Distributed Processing and Applications. ISPA 2006. Lecture Notes in Computer Science, vol 4330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946441_45
Download citation
DOI: https://doi.org/10.1007/11946441_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68067-3
Online ISBN: 978-3-540-68070-3
eBook Packages: Computer ScienceComputer Science (R0)