Enhanced coherent point drift algorithm for remote sensing image registration

Jun Zhang; Lin Lian; Jun Lei; Shuohao Li; Dan Tu

doi:10.1117/1.JRS.9.095074

2 July 2015 Enhanced coherent point drift algorithm for remote sensing image registration

Jun Zhang, Lin Lian, Jun Lei, Shuohao Li, Dan Tu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 095074 (July 2015). https://doi.org/10.1117/1.JRS.9.095074

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.

Citation Download Citation

Jun Zhang, Lin Lian, Jun Lei, Shuohao Li, and Dan Tu "Enhanced coherent point drift algorithm for remote sensing image registration," Journal of Applied Remote Sensing 9(1), 095074 (2 July 2015). https://doi.org/10.1117/1.JRS.9.095074

Published: 2 July 2015

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