International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004 
4. DATA ANALYSIS 
Algorithms are developed and evaluated with simulated signals 
of synthetic objects. 
First, a signal preprocessing of the intensity cube with a 
matched filter is implemented to increase the precision of the 
range measurement and improve the detection rate. These 
results are used to analyze the waveform of each pulse for 
gaining the pulse properties: range, pulse power and number of 
peaks. Then the pulse properties are processed with a region 
based segmentation algorithm. By the use of images the region 
boundary pixels derived from multiple reflections at the same 
spatial position are shared by separate regions. Considering the 
received pulse power of the associated spatial neighborhood for 
the region boundary delivers the estimation of the edge position 
and edge orientation with sub pixel accuracy. 
4.1 Signal preprocessing 
The data analysis starts with the detection of pulses in the 
temporal signal of the intensity cube. Usually this signal is 
disturbed by various noise components: background radiation, 
amplifier noise, photo detector noise etc. Detecting the received 
signal of the pulse in noise and extracting the associated travel 
time of the pulse is a well-known problem and is discussed in 
detail in radar techniques (Mahafza, 2000; Osche, 2002; 
Skolnik, 1980) and system theory (Papoulis, 1984; Turin, 1960; 
Unbehauen, 1996). Due to this problem matched filters are 
used. 
To improve the range accuracy and the signal-to-noise ratio 
(SNR) the matched filter for the signal of the backscattered 
pulse has to be determined. In practice, it is difficult to 
determine the optimal matched filter. In cases where no optimal 
matched filter is available, sub-optimum filters may be used, 
but at the cost of decreasing the SNR. With the assumption that 
the temporal deformation of the. received signal can be 
neglected and the waveform is uniformly attenuated (isotropic 
attenuation by reflection or transmission of the pulse) the signal 
of the emitted pulse is the best choice for the matched filter 
determination. 
Let us further assume that the noise components of the system 
mentioned above are sufficiently described by white noise with 
the constant factor Ny. Furthermore the signal energy of the 
pulse is known as £, and the maximum SNR occurs if the signal 
and the filter match. In this case the associated travel time / of 
the delayed pulse is 79 and the SNR is described by 
2E 
SNR(tg) = — (1) 
No 
An interesting fact of this result is that the maximum of the 
instantaneous SNR depends only on the signal energy of the 
emitted pulse and the noise, and is independent of the 
waveform. 
The matched filter is computed by the cross-correlation Ry 
between the signal of the emitted pulse x and the received 
signal s. We obtain the output signal v with a local maximum at 
the delay time r. 
yn =R (1-1) (2) 
Then the output signal with improved SNR is analyzed by a 
detection filter tor local maxima to determine the travel time of 
the pulse. By using the correlation signal for processing the 
travel time a higher accuracy is reached than by operating on 
the waveform. This is because the specific pulse properties (e.g. 
asymmetric shape, spikes) are taken into account and so less 
temporal jitter can be expected. For the detection the 
preservation of the waveform has no relevance, only 
maximizing the SNR is important. 
Furthermore, the range resolution of the laser scanning system 
generally depends on the temporal width of the emitted pulse. 
Short pulses decrease the average signal power and the 
associated SNR. To increase the temporal pulse width 7 without 
decreasing range resolution we need to consider pulse 
compression. The typical time-bandwidth product of a 
conventional system is BT 1, but with an increasing bandwidth 
B of the receiver and a corresponding waveform the range 
resolution AR is improved and we obtain with the speed of 
light c 
AR=—. (3) 
Then the improved range resolution of the laser scanner system 
is inversely proportional to the bandwidth. 
4.2 Pulse property extraction 
Depending on the size of the focused surface geometry in 
relation to the laser beam (footprint and wavelength) different 
properties can be extracted (Jutzi & Stilla, 2003b). In this paper 
we focus on the properties range, pulse power and number of 
peaks. 
e The range value is processed to determine the distance 
from the system to the illuminated surface. 
e The pulse power value is computed to get a description 
for the reflectance strength of the illuminated area. 
e The number of peaks is considered to locate the 
boundaries of an object with multiple reflections. 
For determining the property values of each pulse the intensity 
cube is processed in different ways. First the range value is 
found from the measured travel time of pulse by the matched 
filter. Then the travel time is used to locate the area of interest 
in the intensity cube for temporal signal analysis. We integrate 
this area with the received signal s for the pulse power value P 
0T. 
P=— [toa . (4) 
as 
Integrating the intensity over a small area instead of quantizing 
a single value has the advantage of decreasing the noise 
influence (Vosselman, 2002). 
Finally the number of peaks is determined by the number of 
detected reflections for each emitted pulse. The number of 
detected pulses depends on the threshold adjustment for the 
local maximum detection of the matched filter output signal. 
4.3 Segmentation 
The segmentation of the range values provides a higher level 
model formation. Segmentation considers the spatial 
neighborhood of detected pulses to generate surface primitives 
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