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Intelligent Recognition of Point Source Target Image Control Points with Simulation Datasets

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Pattern Recognition (ACPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13189))

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Abstract

The point source target (PST) can provide high object and image positioning accuracy and is expected to play an important role in the precise geometric processing of optical remote sensing sensors in the future. This paper proposes a method for intelligently recognizing PST image control points (ICPs) from satellite imagery, which can improve the intelligent level of geometric processing of optical remote sensing sensors. Two deep convolutional neural networks (DCNNs), Faster R-CNN and CenterNet are selected to complete the recognition task. Due to the lack of training data, a large number of simulated samples are generated considering the PST image characteristics. The simulated and real PST ICPs are then used to test the trained DCNNs. The two DCNNs complete the recognition task on the simulated dataset successfully. The Recall and Precision values of the two DCNNs are close to 100%. The performance of the two DCNNs on real PST ICPs is worse than that on the simulated data, but the recognition task is also well completed when the quality of PST ICPs is good. The Recall values of both models are above 95%, and the Precision values are close to 100%. Experiment results also show that the performance of CenterNet is better than Faster R-CNN and the image quality has a great impact on the recognition performance.

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Authors and Affiliations

  1. Academy of Military Sciences, Academy of System Engineering, Beijing, China

    Kai Li & Weiming Yang

  2. School of Geospatial Information, Strategic Support Force Information Engineering University, Zhengzhou, China

    Lei Zhang & Zhenchao Zhang

Authors
  1. Kai Li
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  2. Weiming Yang
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  3. Lei Zhang
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  4. Zhenchao Zhang
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Corresponding author

Correspondence to Kai Li .

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Editors and Affiliations

  1. Korea University, Seoul, Korea (Republic of)

    Christian Wallraven

  2. Nanjing University, Nanjing, China

    Qingshan Liu

  3. Osaka University, Osaka, Japan

    Hajime Nagahara

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Li, K., Yang, W., Zhang, L., Zhang, Z. (2022). Intelligent Recognition of Point Source Target Image Control Points with Simulation Datasets. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13189. Springer, Cham. https://doi.org/10.1007/978-3-031-02444-3_29

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  • DOI: https://doi.org/10.1007/978-3-031-02444-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02443-6

  • Online ISBN: 978-3-031-02444-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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