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Area of Science:

Optics and Photonics
Remote Sensing
Spectroscopy

Background:

Hyperspectral LiDAR combines 3D geometry mapping with spectral reflectance for advanced remote sensing.
Existing methods offer limited precision for long-range measurements.
Automated point cloud segmentation is crucial for data analysis.

Purpose of the Study:

To develop a novel hyperspectral LiDAR system for high-precision 3D surface geometry and spectral reflectance mapping.
To achieve measurement precision below 0.1 mm at ranges up to 50 m.
To enable automated material classification within 3D point clouds.

Main Methods:

Utilized a supercontinuum (SC) laser source coherently broadened from a 780 nm frequency comb.
Employed differential phase delay monitoring of intermode beat notes for distance measurements.
Acquired backscattered light spectra using a CCD spectrometer with 0.16 nm resolution.

Main Results:

Demonstrated measurement precision below 0.1 mm for diffuse targets up to 50 m.
Achieved relative accuracy on the order of 10^-5 compared to a reference interferometer.
Showcased initial success in spectrum-based material classification using a linear support vector machine.

Conclusions:

The proposed hyperspectral LiDAR approach offers significant improvements in precision and accuracy.
This technology has strong potential for integrated high-precision laser scanning and automated material classification.
The findings pave the way for more advanced remote sensing and 3D mapping applications.