Multispectral LiDAR enables joint observations of the 3D geometry and material properties of natural targets by combining ToF-based distance measurements with remote spectroscopy. Established multispectral LiDAR solutions provide mm-level range resolution and reflectance estimates of the target material over some tens of spectral channels. We propose a novel multispectral LiDAR approach based on an ultra-broadband frequency comb that enables enhanced remote spectroscopy by resolving relative delays in addition to reflectance. The spectrally-resolved delay and power measurements are transformed into distance and reflectance spectra by differential observations to a common reference object and adequate system calibration. These distance and reflectance spectra encode material information related to the surface and sub-surface composition and small-scale geometry. We develop the proposed comb-based multispectral LiDAR on an implementation covering the spectral range between 580 nm and 900 nm on 2 different spectral configurations with 7 and 33 channels of different spectral width. The performance assessment of the implemented system demonstrates a distance measurement precision better than 0.1 mm on most channels. Table-top probing results on five material specimens show that both the distance and the reflectance spectra alone enable discrimination of material specimens, while the novel distance signature particularly complements reflectance and increases classification accuracy when the material surface exhibits significant reflectance inhomogeneity. Material classification results using a support vector machine with radial basis function kernel demonstrate the potential of this approach for enhanced material classification by combining both signature dimensions.

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 Li-DAR measurements is also analyzed, with results indicating their capability to support challenging classification tasks.

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.

We demonstrate LiDAR-based remote spectrometry of natural targets augmented with delay spectra using an ultra-broadband frequency comb. Material-dependent spectrally-resolved delays with an equivalent sensitivity better than 100 µm complement reflectance signatures for enhanced target classification.