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    This study estimates surface properties like refractive index and roughness using a modified Bidirectional Reflectance Distribution Function (pBRDF) model and polarimetric images. The novel approach successfully differentiates objects in outdoor scenes based on these derived surface parameters.

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    Area of Science:

    • Optics
    • Remote Sensing
    • Materials Science

    Background:

    • Accurate characterization of surface properties is crucial for remote sensing applications.
    • Polarimetric imaging offers rich information about surface characteristics.
    • Existing Bidirectional Reflectance Distribution Function (pBRDF) models may not fully capture complex surface scattering phenomena.

    Purpose of the Study:

    • To develop and apply a modified pBRDF model incorporating diffuse scattering for enhanced surface parameter estimation.
    • To generate parameter-based images from multispectral Stokes imagery for outdoor target scenes.
    • To validate the model's ability to differentiate objects and provide physically meaningful surface property estimates.

    Main Methods:

    • A modified pBRDF model was developed, including a diffuse scattering component.
    • The model was applied to time sequences of polarimetric images.
    • Multispectral Stokes imagery from the Ground Multiangle Spectro-Polarimetric Imager was utilized.
    • Parameter-based images of complex refractive index, slope variance roughness, and diffuse scattering coefficients were generated.

    Main Results:

    • The modified pBRDF model successfully estimated key surface parameters.
    • Generated parameter-based images revealed distinct differences between various objects in outdoor scenes.
    • The derived parameter values aligned with expected physical properties of the object surfaces.
    • This marks the first application of such a model to multispectral Stokes imagery for this purpose.

    Conclusions:

    • The modified pBRDF model provides a robust method for estimating surface optical and roughness properties from polarimetric data.
    • The generated parameter-based images enhance object discrimination in complex outdoor environments.
    • The approach demonstrates significant potential for advanced remote sensing and surface characterization.