Presentation + Paper
20 May 2022 Polarimetric femtosecond-laser LiDAR for multispectral material probing
Author Affiliations +
Abstract
Polarimetric LiDAR combines polarimetry and non-coherent optical ranging techniques to complement the acquisition of geometrical information with material characteristics. In recent decades, polarimetric LiDAR has been widely explored in material probing, target detection, and object identification. These approaches have so far mainly relied on implementations using a single or very few wavelengths. In this work, we propose, develop and evaluate a polarimetric femtosecond-laser LiDAR that enables extracting multispectral polarization signatures on 7 spectral channels of 40 nm spectral bandwidth and 33 spectral channels of 10 nm spectral bandwidth in the visible and near-infrared range. Multispectral polarization signatures of five material specimens (cardboard, foam, plaster, plastic, and wood board) are obtained and used as input features on a linear support vector machine classification algorithm. The results show that extending polarimetric probing to multiple spectral channels improves the classification capabilities with respect to single-wavelength approaches. The combination of different spectral signature dimensions (polarization, reflectance, and distance) that can be derived from LiDAR measurements is also analyzed, with results indicating their capability to support challenging classification tasks.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yu Han, David Salido-Monzú, Jemil Avers Butt, and Andreas Wieser "Polarimetric femtosecond-laser LiDAR for multispectral material probing", Proc. SPIE 12137, Optics and Photonics for Advanced Dimensional Metrology II, 121370B (20 May 2022); https://doi.org/10.1117/12.2624458
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KEYWORDS
Polarization

Polarimetry

LIDAR

Classification systems

Active remote sensing

Femtosecond frequency combs

Hyperspectral target detection

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