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Journal of Applied Remote Sensing

Enhanced coherent point drift algorithm for remote sensing image registration
Author(s): Jun Zhang; Lin Lian; Jun Lei; Shuohao Li; Dan Tu
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Paper Abstract

Remote sensing image registration is a key component in many computer vision tasks since it can improve the understanding of information among multisensor images through fusing. After feature detection, the image registration is converted into a point set registration problem. The coherent point drift (CPD) algorithm is regarded as a powerful approach for point set registration. However, for junction set, a serious problem arises when using this algorithm—the structural information of the junction is not included in the Gaussian mixture model. To solve this problem, we present an enhanced coherent point drift (ECPD) algorithm. According to the inherent characteristic of junction, we propose the definition of local structural consistency which measures the similarity between two junctions. Furthermore, we introduce local structural consistency as a part of GMM components’ posterior probabilities to achieve more accurate registration results. The experiments of remote sensing image registration show that the ECPD algorithm is more robust to noises and outliers than CPD and outperforms current state-of-the-art methods.

Paper Details

Date Published: 2 July 2015
PDF: 15 pages
J. Appl. Remote Sens. 9(1) 095074 doi: 10.1117/1.JRS.9.095074
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Jun Zhang, National Univ. of Defense Technology (China)
Lin Lian, National Univ. of Defense Technology (China)
Jun Lei, National Univ. of Defense Technology (China)
Shuohao Li, National Univ. of Defense Technology (China)
Dan Tu, National Univ. of Defense Technology (China)


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