Abstract
Image registration is an important preprocessing procedure for remote sensing image applications, such as geometric correction, change detection, and image fusion. Since it is a time-consuming and labor-intensive task to correctly register the remote sensing image, this paper proposes a fully automatic and robust approach for the remote sensing image registration. First, the image pyramid of working and reference images are constructed for coarse to fine matching processing. Second, the feature points can be automatically extracted from the reference image, and the matching point can be searched on the working image. Third, in order to improve the accuracy of registration, the robust estimation serves as an important tool in preserving the correctly matched points. Three sets of satellite images, which include multi-sensor, multi-temporal and multi-spectrum images, are used to test the proposed approach. Results show that the approach is capable of automatically registering the working image to the reference image with great precision.
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Li, W., Leung, H.: A Maximum Likelihood Approach for Image Registration Using Control Point And Intensity. IEEE Transactions on image processing 13(8), 1115–1127 (2004)
Mao, Z., Pan, D., Huang, H., Huang, W.: Automatic registration of SeaWiFS and AVHRR imagery. Int. J. Remote Sensing 22(9), 1725–1735 (2001)
Chen, L.C., Teo, T.A., Rau, J.Y.: Optimized patch back-projection in ortho-rectification for high resolution satellite images. In: IAPRS, pp. 586–591 (2004)
Mäkelä, T., Clarysse, P., Sipilä, O., Pauna, N., Pham, Q.C., Katila, T., Magnin, I.E.: A Review of Cardiac Image Registration Methods. IEEE Transactions on medical image 21, 1011–1021 (2002)
Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-D point sets. IEEE Transactions on Pattern Anal. Machine Intell. PAMI-9, 698–700 (1987)
Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Anal. Machine Intell. 13, 376–380 (1991)
Van Den Elsen, P.A., Pol, E.D., Sumanaweera, T.S., Her, P.F., Napel, S., Adler, J.R.: Grey value correlation techniques used for automatic matching of CT and MR brain and spine images. In: Proc. SPIE Visualization in Biomedical Computing, vol. 2357 pp. 227–237 (1994)
Netanyahu, N., Le Moigne, J., Masek, J.: Geo-Registration of Landsat Data by Robust Matching of Wavelet Features. IEEE Transactions on Geoscience and Remote Sensing 42(7), 1586–1600 (2004)
Stone, H.S., Le Moigne, J., McGuire, M.: Image Registration Using Wavelet Techniques. In: Proceedings of SPIE, vol. 3240, pp. 116–125 (1998)
Thevenaz, P., Ruttimann, U.E., Unser, M.: A pyramid approach to sub-pixel registration based on intensity. IEEE Transactions on image processing 7, 27–41 (1998)
Chen, L.C., Lee, L.H.: Progressive Generation of Control Frameworks for Image Registration. Photogrammetric Engineering and Remote Sensing 58(9), 1321–1328 (1992)
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Chen, CF., Chen, MH., Li, HT. (2007). Fully Automatic and Robust Approach for Remote Sensing Image Registration. In: Rueda, L., Mery, D., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2007. Lecture Notes in Computer Science, vol 4756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76725-1_92
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DOI: https://doi.org/10.1007/978-3-540-76725-1_92
Publisher Name: Springer, Berlin, Heidelberg
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