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WEI Shuo, ZHAO Nanxiang, LI Minle, HU Yihua. Single photon denoising algorithm combined with improved DBSCAN and statistical filtering[J]. LASER TECHNOLOGY, 2021, 45(5): 601-606. DOI: 10.7510/jgjs.issn.1001-3806.2021.05.011
Citation: WEI Shuo, ZHAO Nanxiang, LI Minle, HU Yihua. Single photon denoising algorithm combined with improved DBSCAN and statistical filtering[J]. LASER TECHNOLOGY, 2021, 45(5): 601-606. DOI: 10.7510/jgjs.issn.1001-3806.2021.05.011
shu

Single photon denoising algorithm combined with improved DBSCAN and statistical filtering

  • 1.

    State Key Laboratory of Pulsed Power Laser Technology, School of Electronic Warfare, National Defense University, Hefei 230037, China

  • 2.

    Advanced Laser Technology Laboratory of Anhui Province, School of Electronic Warfare, National Defense University, Hefei 230037, China

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  • Received Date: September 28, 2020
  • Revised Date: December 06, 2020
  • Published Date: September 24, 2021
    Graphical Abstract
    • Abstract
  • In order to solve the problem of excessive noise point clouds in the photon counting lidar detection data, a single photon point cloud denoising method based on a combination of improved density-based spatial clustering of applications with noise (DBSCAN) algorithm and statistical filtering algorithm was adopted. The actual flight data of multiple altimeter beam experimental lidar provided by National Aeronautics and Space Administration was experimental data. First, the point cloud density was obtained through the k-dimensional tree for rough denoising, and then the improved DBSCAN algorithm and statistical filtering algorithm were used for fine denoising. The theoretical analysis and experimental verification has achieved good results. The results show that the target point cloud recognition rate in the experimental area is above 85%, and the performance is better than the classic radius filtering algorithm. This result is helpful for photon data denoising.
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    • References (21)
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