Exploring Weak and Overlapped Returns of a Lidar Waveform With a Wavelet-Based Echo Detector

Abstract

Full waveform data recording the reflected laser signal from ground objects have been provided by some commercial airborne LIDAR systems in the last few years. Waveform data enable users to explore more information and characteristics of the earth surface than conventional LIDAR point cloud. An important application is to extract extra point clouds from waveform data in addition to the point cloud generated by the online process of echo detection. Some difficult-to-detect points, which may be important to topographic mapping, can be rediscovered from waveform data. The motivation of this study is to explore weak and overlapped returns of a waveform. This paper presents a wavelet-based echo detection algorithm, which is compared with the zerocrossing detection method for evaluation. Some simulated waveforms deteriorated with different noises are made to test the limitations of the detector. The experimental results show that the wavelet-based detector outperformed the zero-crossing detector in both difficult-to-detect cases. The detector is also applied to a real waveform dataset. In addition to the total number of echoes provided by the instrument, the detector found 18% more of echoes. The proposed detector is significant in finding weak and overlapped returns from waveforms.

Mapping CHM and LAI for Heterogeneous Forests Using Airborne Full-Waveform LiDAR Data

“…Under this circumstance using waveform LiDAR will increase the number of ground points by about 20% (Fig. 3), so that the DEM quality can be improved (Wang 2012). The differences between DEMs generated from discrete-echo and waveform data could be up to a couple meters in some dense vegetation areas (Fig.…”

“…The refined point clouds were also extracted from waveform data using the wavelet-based echo detector (Wang 2012). Since the Riegl system records waveform data directly without performing echo detection, the delivered point cloud data from Riegl software are actually derived from waveform data.…”

“…The unique advantage of airborne LiDAR over optical and radar sensors is its vegetation penetration capability (Baltsavias 1999). This penetration characteristic especially enables the collection of terrain elevation under a forest canopy, so that it improves the accuracy of the derived digital elevation model (DEM) (Wang 2012). Forest structure and biomass studies benefit greatly from airborne LiDAR systems (Nelson et al 1988;Vargas et al 2002;Popescu et al 2011).…”

“…Recent advances in laser scanning technology have rendered commercial airborne full-waveform LiDAR systems, which are capable of recording the complete waveform of each returning signal (Wagner et al 2006;Alexander et al 2010). For the forest area dataset one may retrieve about 20% more ground points using a refined waveform data echo detector (Wang 2012), so that the DEM generation quality can be maintained. This is a significant merit of using waveform data.…”

A Study on Factors Affecting Airborne LiDAR Penetration

“…By sampling the echo peaks users can record the distance using the LiDAR instrument by referring to the time that it takes for the signal to reach the ground object and return to the instrument. The analysis and fitting of waveforms are thus critical, for which the Gaussian distribution (Hofton et al 2000;Wagner et al 2006;Lin et al 2008;Mallet et al 2008), log normal distribution (Chauve et al 2007), wavelet analysis (Molnar et al 2011;Wang 2012) are frequently used. According to propositions from Hofton et al (2000) and Jutzi and Stilla (2005), the Gaussian decomposition method can improve range measurement accuracy compared with algorithms using only single values.…”