Classification of material and surface roughness using polarimetric multispectral LiDAR

Abstract. 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.