Multi-sensor image registration using multi-resolution shape analysis
Multi-sensor image registration has been widely used in remote sensing and medical image field, but registration performance is degenerated when heterogeneous images are involved. An image registration method based on multi-resolution shape analysis is proposed in this paper, to deal with the problem that the shape of similar objects is always invariant. The contours of shapes are first detected as visual features using an extended contour search algorithm in order to reduce effects of noise, and the multi-resolution shape descriptor is constructed through Fourier curvature representation of the contour’s chain code. Then a minimum distance function is used to judge the similarity between two contours. To avoid the effect of different resolution and intensity distribution, suitable resolution of each image is selected by maximizing the consistency of its pyramid shapes. Finally, the transformation parameters are estimated based on the matched control-point pairs which are the centers of gravity of the closed contours. Multi-sensor Landsat TM imagery and infrared imagery have been used as experimental data for comparison with the classical contour-based registration. Our results have been shown to be superior to the classical ones.
Key wordsImage registration Shape descriptor Feature matching Multi-resolution representation
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- Belongie, S., Malik, J., Puzicha, J., 2001. Matching Shapes. International Conference on Computer Vision (ICCV’01), Canada, 1:454–462.Google Scholar
- Fonseca, L.M.G., Manjunath, B.S., 1996. Registration techniques for multisensor remotely sensed imagery. Photogrammetry Engineering and Remote Sensing Journal, 562(9):1049–1056.Google Scholar
- Haralick, R.M., Shapiro, L.G., 1993. Computer and Robot Vision. Addison-Wesley, MA, 1:57–68.Google Scholar
- Irani, M., Anandan, P., 1998. Robust Multi-Sensor Image Alignment. Sixth International Conference on Computer Vision, Bombay, India, p.959–966.Google Scholar
- Li, Q., Zheng, N.N., Ma, L., Cheng, H., 2004. Principal Component Analysis Neural Network Based Probabilistic Tracking of Unpaved Road. International Symposium on Neural Networks, Dalina, China, p. 792–797.Google Scholar
- Rui, Y., He, L.W., Gupta, A., Liu, Q., 2001. Building an Intelligent Camera Management System. Proceedings of the Ninth ACM International Conference on Multimedia, Ottawa, Canada, p. 2–11.Google Scholar
- Rignot, E.J.M., Kowk, R., Curlander, J.C., Pang, S., 1991. Automated multisensor registration: Requirements and techniques. Photogrammetry Engineering and Remote Sensing Journal, 57(8):1029–1038.Google Scholar
- Worring, M., 1993. Shape Analysis of Digital Curves. Ph.D Thesis. University of Amsterdam.Google Scholar