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    Area of Science:

    • Optics and Photonics
    • Remote Sensing
    • Materials Science

    Background:

    • Distant object imaging is hindered by diffuse speckle patterns, limiting object discrimination.
    • Atmospheric transmission and scattering create complex speckle characteristics in remote target imaging.

    Purpose of the Study:

    • To develop a staring spectral video imaging system for detecting and analyzing high orbit space objects.
    • To establish a mathematical model for remote target scattering spectrum imaging based on speckle phenomena.
    • To invert and identify target materials using spectral image analysis.

    Main Methods:

    • Utilized a ground-based telescope with a staring spectral video imaging system.
    • Developed a mathematical model considering BRDF, Rayleigh scattering, and memory effect for speckle analysis.
    • Employed radial basis function (RBF) neural networks for spectral inversion of global and texture block speckles.

    Main Results:

    • Global inversion identified limited materials (GaAs, CF) with initial fit of 77.97%.
    • An improved algorithm enhanced fit to over 90% by accounting for unknown materials.
    • Texture block analysis revealed diverse materials and varying area ratios within target components.

    Conclusions:

    • Speckle images contain fixed spectral projection structures of distant targets and their components.
    • The developed system and model support remote imaging and identification of challenging, ultra-diffractive targets.
    • Spectral image fine inversion shows potential for restoring clear target structures.