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Journal of Applied Remote Sensing

Robust image registration using adaptive coherent point drift method
Author(s): Lijuan Yang; Zheng Tian; Wei Zhao; Jinhuan Wen; Weidong Yan
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Paper Abstract

Coherent point drift (CPD) method is a powerful registration tool under the framework of the Gaussian mixture model (GMM). However, the global spatial structure of point sets is considered only without other forms of additional attribute information. The equivalent simplification of mixing parameters and the manual setting of the weight parameter in GMM make the CPD method less robust to outlier and have less flexibility. An adaptive CPD method is proposed to automatically determine the mixing parameters by embedding the local attribute information of features into the construction of GMM. In addition, the weight parameter is treated as an unknown parameter and automatically determined in the expectation-maximization algorithm. In image registration applications, the block-divided salient image disk extraction method is designed to detect sparse salient image features and local self-similarity is used as attribute information to describe the local neighborhood structure of each feature. The experimental results on optical images and remote sensing images show that the proposed method can significantly improve the matching performance.

Paper Details

Date Published: 20 May 2016
PDF: 14 pages
J. Appl. Rem. Sens. 10(2) 025014 doi: 10.1117/1.JRS.10.025014
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Lijuan Yang, Northwestern Polytechnical Univ. (China)
Zheng Tian, Northwestern Polytechnical Univ. (China)
State Key Lab. of Remote Sensing Science (China)
Wei Zhao, Northwestern Polytechnical Univ. (China)
Jinhuan Wen, Northwestern Polytechnical Univ. (China)
Weidong Yan, Northwestern Polytechnical Univ. (China)

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