International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
EXPLORING WEAK AND OVERLAPPED RETURNS OF A LIDAR WAVEFORM WITH 
A WAVELET-BASED ECHO DETECTOR 
C. K. Wang 
Dept. of Geomatics, National Cheng Kung University, No. 1, University Road, Tainan, 701, Taiwan - 
p6896102@mail.ncku.edu.tw 
Commission VII, WG VII/7 
KEY WORDS: LIDAR, Detection, Algorithm, Simulation, Accuracy 
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 zero- 
crossing 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. 
1. INTRODUCTION 
Studies have demonstrated that an airborne LiDAR system 
(ALS) can provide accurate estimation of topographic surfaces. 
An important application is for exploring some key 
characteristics of a forest such as canopy height, vertical 
distribution, above-ground biomass, leaf area indices and terrain 
models (Pirotti, 2011). Most of papers, however, have shown 
that LIDAR data underestimates the canopy height (Chauve et 
al., 2009). The reason is explained that only a small proportion 
of laser pulses interacting with the tree apices. Low point 
density of tree tops results in underestimation of tree heights. 
Another tricky problem is to classify the ground points in a 
complex forest area. Usually when a laser emits into a forest 
area, only some of the laser energy is scattered back by the tree 
tops. The other energy penetrates through and is reflected by 
branches, shrubs and the ground. The received echo scattered by 
the ground could be too weak to being detected (Figure 1 (a)). 
Additionally if scatters are separated around the range 
resolution of the instrument, for example, the terrain and shrubs, 
an ALS will produce a waveform composed of a superposition 
of echoes. The overlapped echoes are difficult to resolve and 
usually only an uncertainty point located between the terrain 
and shrubs is detected (Mallet and Bretar, 2009) (Figure 1(b)). 
The terrain height represented by this point will be a slight 
higher than the true terrain. Fortunately, thanks to the 
development of LiDAR technology, the laser scanner systems 
with full waveform digitizing capabilities have become 
available. Compared with the conventional LiDAR system, 
waveform LiDAR further encodes the intensity of the reflected 
energy along the laser lighting path. Users now can utilize the 
waveforms for the interpretation of ground objects and develop 
their methods to detect the effective return echoes. This gives us 
a great opportunity to improve the canopy height estimation and 
terrain model if an approach which can deal with the weak and 
overlapped echoes is obtained. 
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(b) 
  
  
   
Figure 1. Interpretation of difficult-to detect echoes, (a) weak 
echo due to the loss of laser energy, (b) overlapped echoes due 
to the scatters separated around the range resolution of systems. 
The purpose of this study is to explore extra return echoes in 
waveform data, especially weak and overlapped echoes which 
are not detected in the online process of an ALS. However, 
weak echoes are difficult to detect due to the low signal-to-noise 
(SNR), and overlapped echoes are also hard to resolve because 
they are synthesized signal. To deal with this problem, one has 
to extract signals from a waveform that is composed of echo 
signals and data noises. This paper proposes a wavelet-based 
detector for the solution. The detector first applies a wavelet 
transform (WT) to decompose a LiDAR waveform in terms of 
elementary contributions over dilated and translated wavelets, 
and then searches possible echoes in the wavelet coefficients 
(WC) at a certain scale. Using the WT of a LiDAR waveform 
enables us to detect the echoes at various scales and to suppress 
the noises simultaneously(Shao et al, 1998). The detector 
detects all possible return echoes from a waveform signal. 
Focused on the detection of weak and overlapped echoes, our 
analysis aims at how much signal strength relative to noise and