DECOMPOSITION OF AIRBORNE LASER SCANNING WAVEFORM DATA BASED ON 
EM ALGORITHM 
Qi Li* 
School of Remote Sensing and Information Engineering, Wuhan University. 129 Luoyu Road, Wuhan 430079 
KEY WORDS: LIDAR, Waveform-digitizing, Analysis of waveform, Filter, Smooth, Gaussian decomposition, DSM, DTM, Forest 
structure 
ABSTRACT: 
Data storage capacity and high processing speed available today has made it possible to digitally sample and store the entire reflected 
waveform of Small-Footprint Airborne Lidar (light detection and ranging), instead of only extracting the discrete coordinates which 
form the so-called point clouds. One of the most important advantages from waveforms data is that it gives the user the chance to 
extract three-dimensional coordinates by himself in the post-processing. Decomposition return waveform is a key step during 
analyzing waveform data. Conventional algorithm to decompose is maximum and centre of gravity, or simply by using the 
thresholding method provided by equipment vendor. Both show lack of high accuracy. In this paper, an improved Expectation 
Maximum (EM) algorithm is adopted to extract peak location and pulse width from raw waveform data, proving it is a reliable and 
high accurate decomposition algorithm. Moreover, the high-quality point-cloud data could be obtained which provides high-quality 
resources for DSM(Digital Surface Model) and DTM (Digital Terrain Model) production. Derived forest biophysical parameters, 
such as vegetation height and crown volume are able to describe the horizontal and vertical forest canopy structure. 
1. INTRODUCTION 
In the last ten years, airborne laser scanning is a rapidly growing 
technology which has initially been conceived for topographic 
mapping. Airborne laser scanners employ, with few exceptions, 
pulsed lasers that repetitively emit short infrared pulses towards 
the Earth’s surface. Some of the energy is scattered back to the 
sensor where it is measured with an optical receiver. A timer 
measures the traveling time of the pulse from the laser scanner 
to the Earth’s surface and back, the integration of laser with 
GPS and INS for position and orientation determination. 
Nowadays, ALS is used routinely for topographic mapping and 
is considered to hold a large potential in a range of other 
applications such as forestry, 3D city modeling or power line 
detection. 
Development of airborne laser scanning goes back to the 1970s 
and 1980s. The first commercially available airborne laser 
scanners recorded the time of one backscattered pulse. The 
recording of only one pulse is sufficient if there is only one 
target within the laser footprint. State-of-the-art commercial 
laser scanners typically measure first and last pulse; some are 
able to measure up to five pulses. In order to derive digital 
terrain models (DTMs), laser pulses reflected by the ground 
surface must be distinguished from non-terrain points. This task 
can be achieved using various filtering techniques that classify 
the point cloud into terrain and off-terrain points just based on 
the spatial relationship of the 3D data. For many applications, 
this has been deemed the suitable form of output. However, the 
user has no way of knowing how the electronics of his LIDAR 
system actually determine the location of the returns they report, 
nor of any distortions of the pulse shape that receiver electronics 
or surface structures may have imposed upon the pulse echo. 
LIDAR system manufacturers are tight-lipped about the pulse 
detection methods their systems employ. However, as Wagner 
et al. (2004) point out the choice of pulse detection methods has 
significant impact on accuracy, and in practice causes a number 
of effects that reduce the quality of the measurements, like 
amplitude dependant range walk, slope dependency of range, 
signal ringing causing outlier measurements below the terrain 
level, etc. In addition, with mere range output much of the 
informational content about structured surfaces is lost. 
The solution is to digitally sample and store the entire echo 
waveform of reflected laser pulses. Digitizing and recording the 
complete backscattered waveform during the acquisition for 
later post-processing has the advantages that algorithms can be 
adjusted to tasks, intermediate results are respected, and 
neighborhood relations of pulses can be considered. The 
technical feasibility has been demonstrated by large-footprint 
airborne systems developed by NASA in the 1990s, namely the 
Scanning Lidar Imager of Canopies by Echo Recover (SLICER) 
and the Laser Vegetation Imaging Sensor (LV1S). Recently, 
three commercial airborne systems have become available, such 
as Figure 1,namely the RIEGL LMS-Q560> Toposys FalconllL 
Leica ALS-II % Optech ALTM 3100E. 
Discrete 
Returns 
.1 si return 
} 2nd return 
. last return 
time 
Echo 
waveform 
Amplitude 
outgoing pulse 
return signal 
1st level 
(canopy) 
anopy 
structures 
I level (bushes) 
ground 
time 
Fig. 1 waveform Digitization 
An approach based on unsupervised learning is presented where 
a mixture of Gaussian distributions are fitted to the waveforms 
* Email:qli_rs@yahoo.com.cn