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Fast registration of UAV aerial images based on improved optical-flow model combined with feature-point matching

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Abstract

With a large number of registration algorithms proposed, image registration techniques have achieved rapid development. However, there still exist many deficiencies in aerial images registration where high speed and accuracy are difficult to simultaneously achieve for real-time processing. In order to achieve large-scale and high-precision image registration for unmanned aerial vehicle(UAV) aerial images, a novel and fast sub-pixel image registration algorithm based on improved optical-flow model combined with feature-point matching is proposed in this paper. Firstly, the coarse selection at the feature level is achieved by using the feature-point model, which reduces the number of non-feature points so as to speed up the coarse registration process. Then, the improved pyramid optical-flow model is adopted in the neighborhood of the coarse point, and the sub-pixel fast location is achieved by the bidirectional search strategy. Simulation experiment results show that compared with common image registration based LK optical-flow or feature-point matching, our proposed algorithm will greatly reduce space complexity and time complexity without losing accuracy.

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Acknowledgments

National Science Foundation for Young Scientists of China (61703242, 61503224), The first batch of cooperative education projects of the Ministry of education in 2017(201701065008), postgraduate science and technology innovation project of Shandong University of Science and Technology in 2018 (SDKDYC180234).

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Authors and Affiliations

  1. Shandong University of Science and Technology, No.579, Qianwangang Road, Qingdao Economic and Technological Development Zone, Qingdao, 266590, Shandong, China

    Changzhi Lv, Yingshuai Wu, Di Fan & Xiushan Lu

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  1. Changzhi Lv
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  2. Yingshuai Wu
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  4. Xiushan Lu
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Correspondence to Changzhi Lv.

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Lv, C., Wu, Y., Fan, D. et al. Fast registration of UAV aerial images based on improved optical-flow model combined with feature-point matching. Multimed Tools Appl 78, 8875–8887 (2019). https://doi.org/10.1007/s11042-018-6580-6

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