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Angle-independent spectropolarimetric target classification using machine learning.

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    This study introduces a hyper-spectral polarimetric LiDAR system for target characterization. It uses a k-nearest neighbors algorithm to accurately differentiate manmade and natural objects based on light polarization.

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    Area of Science:

    • Remote Sensing
    • Optical Engineering
    • Material Science

    Background:

    • Light polarization differs between manmade and natural objects.
    • Polarization measurements are valuable for remote sensing applications.
    • Differentiating artificial and natural targets is crucial in various fields.

    Purpose of the Study:

    • To present a novel method for characterizing unknown targets.
    • To utilize hyper-spectral polarimetric LiDAR for material classification.
    • To develop a robust algorithm for distinguishing artificial from natural objects.

    Main Methods:

    • Implementation of a hyper-spectral polarimetric light detection and ranging (LiDAR) system.
    • Application of a k-nearest neighbors (KNN) algorithm for material classification.
    • Testing with incident angles from -10° to 60° without target orientation assumptions.

    Main Results:

    • Achieved a balanced accuracy of 86.9% in material classification.
    • Demonstrated effective differentiation of artificial and natural objects using polarization.
    • Validated the KNN algorithm's performance across a range of incident angles.

    Conclusions:

    • Hyper-spectral polarimetric LiDAR is effective for target characterization.
    • Polarization signatures provide a reliable means to distinguish manmade from natural materials.
    • The developed KNN method offers a robust solution for remote sensing material identification.