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    Area of Science:

    • Geospatial science
    • Remote sensing technology
    • Signal processing

    Background:

    • Full-waveform LiDAR is crucial for detailed topographic, forestry, and urban mapping.
    • Existing decomposition methods struggle with asymmetric echo shapes and varied scattering, leading to decomposition errors.

    Purpose of the Study:

    • To develop an Adaptive Asymmetric Gaussian Decomposition (AAGD) method for accurate full-waveform LiDAR echo decomposition.
    • To overcome limitations of symmetric and fixed-parameter asymmetric models in complex scenarios.

    Main Methods:

    • Established a linear relationship between broadening factor and standard deviation ratio.
    • Developed an adaptive parameter adjustment mechanism for echo shape parameters.
    • Integrated Levenberg-Marquardt (LM) optimization for dynamic parameter adjustment.

    Main Results:

    • AAGD achieved 96.08% detection accuracy on simulated data, reducing over-decomposition to 0.40% and under-decomposition to 3.52%.
    • On Global Ecosystem Dynamics Investigation (GEDI) data, AAGD reduced root-mean-square error (RMSE) by 18.08%-41.34% compared to existing methods.

    Conclusions:

    • AAGD demonstrates superior performance in decomposing complex LiDAR echoes under diverse scattering conditions.
    • The method ensures both mathematical precision and physical consistency, improving point cloud quality and feature extraction.