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Surface identification from multiband LADAR reflectance with varied incidence angle via database mapping.

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    This study shows how multiband LADAR (Light Detection and Ranging) can identify surfaces by analyzing reflected radiance. Using stochastic neighborhood embedding (t-SNE) improves surface identification, even with unknown incident angles.

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    Area of Science:

    • Remote Sensing
    • Data Science
    • Materials Science

    Background:

    • LADAR reflection characteristics are influenced by material properties and surface textures.
    • These dependencies can be leveraged for effective surface identification.
    • Multiband LADAR offers potential for enhanced surface characterization.

    Purpose of the Study:

    • To assess surface identification using multiband LADAR reflected radiance.
    • To investigate the effectiveness of stochastic neighborhood embedding (t-SNE) in analyzing LADAR data.
    • To determine if t-SNE can overcome challenges posed by unknown incident angle dependencies.

    Main Methods:

    • Utilized the nonconventional exploitation factors data system database.
    • Applied a statistics-based dimension reduction algorithm, t-SNE, to LADAR reflected radiance and band ratios.
    • Analyzed data clouds generated from monostatic LADAR measurements.

    Main Results:

    • t-SNE effectively separated data clouds from multiband LADAR reflected radiance and band ratios.
    • This separation enabled surface identification despite unknown incident angle dependencies.
    • Significant improvements in surface identification were observed using t-SNE mapping.

    Conclusions:

    • Multiband LADAR reflectance, analyzed with t-SNE, provides a viable method for surface identification.
    • t-SNE enhances the robustness of LADAR-based surface identification against variable incident angles.
    • This approach facilitates accurate surface identification in complex environments.