You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Technical Commission VII

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
Data acquisition was performed by using Leica's ALS60 sensor
with WDM6S, mounted onboard a Cessna aircraft. WDM65
records the complete waveform of the reflection from the
surface intercepting the laser footprint. The full waveform data
was collected as 256 samples (à) 1ns. Field of View (FOV) of
the scanner was set at 28 degrees, scan rate was set at 33.7 Hz
and laser pulse rate used was 54000 Hz. The data acquisition
was conducted by flying the aircraft at an altitude of 2491 m
from the mean sea level. The terrain height of the area varied
from 439 m to 790 m. The average point density of the
transmitted pulse was 1point/sq.m.
A computer program was developed to generate georeferenced
point data from the full waveform return pulses. The point
cloud generation algorithm implemented in our approach is
explained below with an illustration of a typical case.
(1) Discrete return pulse (black square dot) and the
corresponding waveform data for the laser pulse are depicted in
Figure 2 (top). The blue dots in the top image in Figure 2 are
full waveform data plotted against time. As seen in the figure,
the full waveform data are generally embedded with noise
generated from the variety of sources. Hence, a 12 order Finite-
duration Impulse Response (FIR) zero-phase digital filter
technique was applied to smooth the waveform and to reduce
the noise for the further analysis. For a 12 order FIR fiiter
(Figure 3), the output is a weighted sum of the current and a
finite number of previous values of the input as described in
equation 1.
y(n) 2 x(n)x h(0) - x( n -1)x h(1) ---- x(n-12)x h(12) (1)
The red curve in Figure 2 (top) is the smooth waveform data
obtained after the processing. The smooth waveform obtained
from this technique preserves peak position, pulse width and
skew of the laser reflection (Wong and Antoniou, 1994).
(2) After the waveform smoothening, the next step is to
distinguish the noise from the actual return intensity. For that
purpose, we selected a threshold and assumed all the reflection
below the given threshold as noise and excluded those values
for further processing. This is a very straight forward approach
and computationally very efficient. Two threshold values of
intensity 16 and 17 were tested. When the threshold was set at
16, in some cases the points below ground level under the tree
canopies were also generated. However with threshold 17, the
last return peak of the waveform corresponds very well with the
ground position level, under the tree canopies. Hence for the
surveyed region, all the waveform return intensity below 17
was assumed noise and considered their intensity to be 0.
(3) In the third step, the number of peaks and the location of the
peaks in the smooth waveform data were, at first, estimated by
finding the local maxima in the smooth waveform. Then an
algorithm based on the Expectation Maximization (EM)
(Persson et.al., 2005) was applied to correct the positions and
number of the peaks by Gaussian decomposition. The crosshair
symbols at the peak of the Gaussian bumps in Figure 2 (top) are
the corrected peak positions estimated by the EM algorithm.
(4) Finally, the correctly estimated peak positions were
georeferenced to create a point cloud data.
The bottom image in Figure 2 shows the discrete LIDAR pulses
(green dots), obtained during September, overlaid on a
Triangulated Irregular Network (TIN) of a bare ground
constructed from the discrete LiDAR pulses obtained during
December. The red dots in the image are the position data
obtained by georeferencing the Gaussian peaks of the smooth
waveform data, depicted by the cross hair symbols in the Figure
2 (top image). From the figure it is clear that, for the particular
laser pulse, the discrete return could record only one reflection
from the top canopy layer however the proposed algorithm
could detect 3 reflections of which the first one corresponds to
the same discrete return and the last one corresponds to the
ground level.


145163" 185 205 225
Time (ns)

E Smooth Curve : Waveform data X GaussianPeak ® Discrete |

Figure 2. Top: Discrete return pulse (black square dot), raw
full waveform data (blue dots), smoothened waveform (red
curve) and Gaussian peaks (crosshair symbols). Bottom:
Discrete return pulses (green dots), full waveform return
pulses (red dots) constructed from the Gaussian peaks
depicted in the top image