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Local convolutional features and metric learning for SAR image registration

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Abstract

The conventional synthetic aperture radar (SAR) image registration focuses on designing diverse hand-crafted features and distance metrics. It is still challenging to obtain accurate feature correspondence when influenced by speckle noise and geometric distortion. This paper proposes local convolutional neural network (CNN) features based method to solve the keypoint matching problem, whose contributions are threefold. (1) a feature descriptor based on local convolutional features of image patches (LCFs-P) is deployed to extract more discriminative features than the conventional CNNs. (2) A new feature correspondence scheme based on metric learning is proposed to boost the feature matching performance. (3) A local geometric similarity method is employed to remove the mismatches of the tentative matches. The experimental results on the real SAR dataset and Middlebury dataset demonstrate that the proposed model outperforms the existing state-of-the-art methods in terms of matching accuracy and efficiency.

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Authors and Affiliations

  1. State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100191, China

    Qiangliang Guo, Jin Xiao & Xiaoguang Hu

  2. School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100191, China

    Baochang Zhang

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  1. Qiangliang Guo
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  2. Jin Xiao
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  3. Xiaoguang Hu
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  4. Baochang Zhang
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Correspondence to Qiangliang Guo.

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Guo, Q., Xiao, J., Hu, X. et al. Local convolutional features and metric learning for SAR image registration. Cluster Comput 22 (Suppl 2), 3103–3114 (2019). https://doi.org/10.1007/s10586-018-1946-0

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  • DOI: https://doi.org/10.1007/s10586-018-1946-0

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