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Multi-spectral SWIR lidar for imaging and spectral discrimination through partial obscurations.

Vasanthi Sivaprakasam, Di Lin, Michael K Yetzbacher

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    We created a multi-spectral SWIR lidar system for object identification. This advanced lidar effectively classifies targets even when partially obscured, outperforming traditional hyperspectral imaging.

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    Area of Science:

    • Optics and Photonics
    • Remote Sensing
    • Spectroscopy

    Background:

    • Traditional imaging techniques struggle with target identification in the presence of obscurants like fog or smoke.
    • Hyperspectral imaging provides spectral information but can be limited in obscured environments.
    • Lidar (Light Detection and Ranging) offers range information, crucial for depth perception and object separation.

    Purpose of the Study:

    • To develop and evaluate a multi-spectral Short-Wave Infrared (SWIR) lidar system.
    • To assess the system's capability for simultaneous spatial-spectral imaging and spectral discrimination through partial obscurations.
    • To demonstrate the advantages of multi-spectral lidar over hyperspectral imaging for target identification under obscured conditions.

    Main Methods:

    • Development of a novel multi-spectral SWIR lidar system.
    • Experimental evaluation using objects placed with and without various obscurants.
    • Application of a principal component analysis (PCA)-based algorithm for object classification.
    • Quantification of detection accuracy under different obscured scenarios.

    Main Results:

    • The multi-spectral SWIR lidar system successfully acquired simultaneous spatial-spectral information.
    • The system demonstrated effective object classification and discrimination through partial obscurations.
    • PCA-based algorithm achieved accurate target detection even in the presence of obscurants.
    • Performance comparison highlighted the superiority of multi-spectral lidar in obscured environments.

    Conclusions:

    • Multi-spectral SWIR lidar is a powerful tool for target identification in challenging, obscured conditions.
    • The developed system offers significant advantages over hyperspectral imaging for applications requiring robust target detection.
    • Spatial-spectral information fusion is key to overcoming limitations imposed by obscurants.