The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008
when the previous pulse recording is effective. Full waveform
LiDAR systems record the entire signal of the backscattered
laser pulse (Figure 1).
Time (ns)
Figure 1: Transmitted and received waveform and the
corresponding range in a complex wooded area with a small
footprint LiDAR system.
The first operational system Laser Vegetation Imaging Sensor
(LVIS), developed by the NASA, appeared in 1999 and
demonstrated the value of recording the entire waveform for
vegetation analysis (Blair et al., 1999). The first commercial
full-waveform LiDAR system was introduced in 2004 (Hug et
al., 2004). Today, most of LiDAR-involved companies (e.g.
Riegl, Optech, Leica, Toposys) propose such an extension to
their multiple pulse devices.
Full waveform systems sample the received waveform of the
backscattered pulse at a maximum frequency of 1 GHz, which is
equivalent to 1 GSamples/s. Such systems differ in sampling
rate, in scan pattern and in footprint size. Most commercial
systems are small-footprint, typically 0.3 - 1 m diameter at 1km
altitude, depending on altitude and beam divergence.
To record full waveform LiDAR data, the main commercial
manufacturers have added digitization terminals to their systems
and increased the storage media capacity. Whatever the LiDAR
system method, the constant digitization sampling period varies
between 1 to 10 ns. The waveform is not integrally recorded but
only for a predefined maximum number of samples. Indeed, it is
necessary to avoid recording too many useless samples because
they result into massive storage problems. For example,
TopoSys ALTM systems can store up to 440 samples for each
pulse. This is equivalent to a continuous vertical section of 66
meters (440 x 0.15 m per sample). The TopEye Markll system
saves 128 samples according to a predefined mode which is
either ’’first pulse and later ” (127 samples after the first) or ’’last
pulse and earlier ” (127 samples before the last detected). This
means that full-waveform systems will not record both the
echoes from the canopy and from the ground within a given
waveform if the trees are taller than the ’’recording length” of
the system.
3. PROCESSING THE WAVEFORMS
the received signal. From the local maxima of the fitted function
the range value is calculated and 3D points can be determined.
Pulse properties (width and amplitude) can be calculated at the
same time. Here, waveform processing consists in decomposing
the signal f(x) into a sum of components f(x) so as to
characterise the different objects along the path of the laser
beam:
V = /O) = ^ fj (s)
7=1
Considered as a sum of Gaussian functions initially in (Hofton
et al., 2000) and (Wagner et al., 2006), various formulations of
fj(x) have been tested in Chauve et al. (2007a): the Gaussian
function (G), the Log normal function (LG) and the Generalized
Gaussian function (GG).
The authors show that the waveform modelling is better using a
GG function. More over, it introduces another pulse feature (a)
that can be integrated in a segmentation process (Section 4.2).
/oj(*) =
= cij exp
h.j(x) =
= ajexp
fGGj(x) =
= Oj exp
An other approach consists in applying signal processing
methods based on the transmitted and the received waveform.
The matched filter is computed by the normalized cross-
Mr)
r oo
I 5
(/) •/•(/+ x)dt
J f s 2 (t)dt ■ f r(t)dt
correlation function R sr between the transmitted waveform s(t)
of the emitted pulse and the received waveform r(t) of the
backscattered pulse.
Here, echoes are local maxima of the correlation functions
(Hofton & Blair, 2002; Kirchhof et al., 2008) .
This approach considers strong echoes for further processing,
but weak echoes have to be detected, revised and processed
again. In order to overcome this problem and retrieve partially
occluded objects, waveforms can no longer be processed
independently: local neighbourhoods have to be introduced.
(Stilla et al., 2007) propose a waveform stacking strategy of
several weak echoes within a local environment to increase the
signal-to-noise ratio.
The processing of waveform data starts in maximizing the
number of relevant detected peaks within each signal.
A first approach consists in deriving a parametric formulation of
Alternatively, the knowledge of the local geometry can be used
to improve the cross-correlation techniques. Considering the
waveform as a convolution between the transmitted waveform