Multispectral light detection and ranging (LiDAR) is an emerging active remote sensing technique that combines distance and spectroscopy measurements. The reflectance spectrum is known to enable material classification. However, the spectrum also depends on other surface parameters, particularly roughness. Herein, we propose an extension of multispectral to polarimetric multispectral LiDAR and introduce unpolarized and linearly polarized reflectance spectra as additional features for classifying materials and roughness. Using a bench-top prototype instrument, we demonstrate the feasibility and benefit of acquiring unpolarized and linearly polarized reflectance spectra. We analyze and interpret the spectra obtained with two different spectral resolutions (10 and 40 nm) from measurements on test specimens consisting of five different materials with two different levels of surface roughness. Using a linear support vector machine, we demonstrate the potential of the different features for enabling material and roughness classification. We find that the unpolarized reflectance spectrum is well suited for classifying materials, and the linearly polarized one for classifying roughness. In both cases, the performance is much better than using a standard reflectance spectrum offered by multispectral LiDAR. We identify polarimetric multispectral LiDAR as a technology that may significantly enhance surface and material probing capabilities for remote sensing applications.
Multispectral LiDAR is an emerging active remote sensing technique that combines distance and spectroscopy measurements on light reflected from the surface at the respective measurement point. It is known that the reflectance spectrum can be used for material classification. However, the spectrum also depends on other surface parameters, particularly surface roughness. Herein, we propose an extension of multispectral to polarimetric multispectral LiDAR and introduce polarized and unpolarized reflectance spectra as additional features for classifying materials and roughness. We demonstrate the feasibility and the benefit using a bench-top prototype instrument which allows acquiring standard, polarized and unpolarized reflectance spectra, in addition to distance, in 33 spectral channels with 10 nm bandwidth between 580 and 900 nm. We analyze and interpret the raw spectra obtained from measurements on test specimens consisting of five different materials (PE, PVC, PP, sandstone, limestone) with two different levels of surface roughness. Using a linear support vector machine (SVM) we demonstrate the potential of the different features for independent material and roughness classification. The results indicate that the unpolarized reflectance spectrum increases the material classification accuracy by 50% as compared to a standard spectrum, and that the polarized spectrum actually allows classifying roughness. We interpret the results as a strong indication that multispectral polarimetric LiDAR enables deriving practically relevant additional information on surfaces with high spatial resolution through remote sensing.
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.
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