Color Restoration for Full-Waveform Multispectral LiDAR Data

Binhui Wang,Xin Lin,Jia Sun,Dong He,Zhenwei Chen,Shalei Song,Faquan Li,Wei Gong,Xiong Cao

doi:10.3390/rs12040593

Abstract

The current full-waveform data at a single wavelength can mainly retrieve the geometric attributes of targets along the light path by detecting waveform components, resulting in the lack of spectral or color attribute information. This kind of device relies on a digital camera for acquiring the color information, however, which is inevitably limited by the lighting conditions and geometric registration errors. With the development of multispectral light detection and ranging (LiDAR) or even hyperspectral LiDAR that often utilize a supercontinuum laser source covering the whole visible light band, including red, green and blue bands, the simultaneous acquisition of color and spatial information becomes possible and makes passive imaging data no longer necessary. In this study, we propose a color restoration method for a full-waveform multispectral LiDAR (FWMSL) system. Additionally, we develop a multispectral lognormal function to fit the tailing echoes measured by FWMSL further accurately. Experimental data from our FWMSL system are used to evaluate the performance of the proposed method. The relative standard deviation, correlation coefficient (R2) and color difference ( Δ E ) metrics suggest that the color restoration for the full-waveform multispectral data is feasible.

Highlights

Light detection and ranging (LiDAR) system has been known as one of the most effective survey tools to characterize vegetation structure for the past decades because of its excellent ranging accuracy and canopy penetration capability [1,2]
This study explores the optimal number of pulse accumulation in order to attenuate random errors and increase signal-to-noise ratio (SNR) of waveform data, improving the accuracy of color restoration
This study proposes a color restoration method for the full-waveform multispectral LiDAR (FWMSL) system, to extract color information from multispectral full-waveform data

Summary

Introduction

Light detection and ranging (LiDAR) system has been known as one of the most effective survey tools to characterize vegetation structure for the past decades because of its excellent ranging accuracy and canopy penetration capability [1,2]. The shape features (e.g., central location, pulse width and amplitude) extracted from waveform data [1,4] are greatly helpful in various applications, such as land cover classification [5], building extraction [6,7], canopy height retrieval [8] and biomass estimation [9]. Reflective and texture features of the target surface are required to meet the application needs as mentioned above. The current full-waveform data at a single wavelength can mainly retrieve the geometric attributes of targets along the light path by detecting waveform components, resulting in the lack of spectral or color attribute information. Many studies have focused on solving the geometric registration problems and achieved high registration accuracy [11,12,13], considerable time and labor are still needed to achieve a satisfactory registration effect

Objectives

Methods

Findings

Discussion

Conclusion