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Received: 2005-02-10

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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.4 P.549~555

http://doi.org/10.1631/jzus.2006.A0549


Multi-sensor image registration using multi-resolution shape analysis


Author(s):  Yuan Zhen-ming, Wu Fei, Zhuang Yue-ting

Affiliation(s):  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   zmyuan@cs.zju.edu.cn, wufei@cs.zju.edu.cn, yzhuang@cs.zju.edu.cn

Key Words:  Image registration, Shape descriptor, Feature matching, Multi-resolution representation


Yuan Zhen-ming, Wu Fei, Zhuang Yue-ting. Multi-sensor image registration using multi-resolution shape analysis[J]. Journal of Zhejiang University Science A, 2006, 7(4): 549~555.

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author="Yuan Zhen-ming, Wu Fei, Zhuang Yue-ting",
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%T Multi-sensor image registration using multi-resolution shape analysis
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%A Wu Fei
%A Zhuang Yue-ting
%J Journal of Zhejiang University SCIENCE A
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%P 549~555
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0549

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T1 - Multi-sensor image registration using multi-resolution shape analysis
A1 - Yuan Zhen-ming
A1 - Wu Fei
A1 - Zhuang Yue-ting
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SP - 549
EP - 555
%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A0549


Abstract: 
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.

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Reference

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