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    Photon counting lidar data needs smoothing to reduce noise. This study introduces Poisson thinning to optimize filter parameters, balancing random and systematic errors for better lidar signal processing.

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    Area of Science:

    • Geospatial science
    • Optical remote sensing
    • Signal processing

    Background:

    • Photon counting lidar (PCL) signals often contain random noise requiring smoothing.
    • Standard smoothing techniques can introduce systematic errors by smearing high-gradient signals.
    • Quantifying and balancing random versus systematic errors in PCL data is challenging and scene-dependent.

    Purpose of the Study:

    • To introduce a novel method for optimizing smoothing filter parameters in photon counting lidar.
    • To enable quantitative evaluation and selection of filter parameters based on scene characteristics.
    • To improve the accuracy of photon counting lidar signal processing.

    Main Methods:

    • Implementation of Poisson thinning for lidar signal processing.
    • Quantitative evaluation of filter parameters using defined criteria.
    • Optimization of smoothing based on scene-dependent error balancing.

    Main Results:

    • Poisson thinning allows for optimal selection of filter parameters for photon counting lidar.
    • This method effectively balances random noise suppression with the minimization of systematic errors from signal smearing.
    • The optimization process is computationally inexpensive and easy to implement.

    Conclusions:

    • Poisson thinning offers a robust solution for noise reduction in photon counting lidar.
    • This approach enhances the reliability and accuracy of lidar data by managing systematic errors.
    • The method provides a scene-adaptive strategy for processing photon counting lidar signals.