Range Resolution Enhanced Method With Spectral Properties for Hyperspectral LiDAR

Abstract

Waveform decomposition is needed as a first step in the extraction of various types of geometric and spectral information from hyperspectral full-waveform light detection and ranging (LiDAR) echoes. We present a new approach to deal with the 'pseudo-monopulse' waveform formed by the overlapped waveforms from multitargets when they are very close. We use one single skew-normal distribution (SND) model to fit waveforms of all spectral channels first and count the geometric center position distribution of the echoes to decide whether it contains multitargets. The geometric center position distribution of the 'pseudo-monopulse' presents aggregation and asymmetry with the change of wavelength, while such an asymmetric phenomenon cannot be found from the echoes of the single target. Both theoretical and experimental data verify the point. Based on such observation, we further propose a hyperspectral waveform decomposition method utilizing the SND mixture model with: 1) initializing new waveform component parameters and their ranges based on the distinction of the three characteristics (geometric center position, pulsewidth, and skew coefficient) between the echo and fit SND waveform; 2) conducting single-channel waveform decomposition (SCWD) for all channels; 3) setting thresholds to find outlier channels based on statistical parameters of all single-channel decomposition results (the standard deviation and the means of geometric center position); and 4) reconducting SCWD for these outlier channels. The proposed method significantly improves the range resolution from 60 to 5 cm at most for a 4-ns width laser pulse and represents the state-of-the-art in 'pseudo-monopulse' waveform decomposition.