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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
5. APPLICATIONS OF SFS FILTER ON LIDAR DATA
The SFS program has been tested both on differently simulated
LIDAR datasets and really measured points acquired with an
Optech? ALTM 3033 airborne system.
5.1 Testing on simulated data
As far as simulated datasets are concerned, a lot of experiments
has been carried on, here reporting 6 tests differing for surface
type (plane and quadratic), for value of spatial interaction p and
for mean noise |g]. In each dataset, with irregularly spaced
points, the presence of some buildings (outliers of the ground
surface) has been simulated. The number of ground and non-
ground points is then exactly known, so that the efficiency of
the algorithm could be easily verified.
General characteristics of these 6 examples, simulating real
survey conditions, are reported in Table 6.
Surface type Plane (=3) | 2™ Order (r=5)
6,=1,000
6,=1,000 6,=+0,005
Polynomial coefficients 6,=0,050 0,=-0,001
6,=-0,010 05—0,0015
6,=-0,002
Uncorrelated noise o, Plan-1:0,10 m | Quad-1: 0,10 m
Plan-2: 0,20 m | Quad-2: 0,20 m
Gyr Surface Plan-3: 0,25. m |_Quad-3: 0,25 m
Plan-1: 0,0 Quad-1: 0,0
Spatial interaction p Plan-2: 0,1 Quad-2: 0,1
Plan-3: 0,2 Quad-3: 0,2
Number of points (n) 1.886
Raw data (not grid) Yes
Points sampling 1 point/m (mean)
Dataset area 1.760 m*
AZ 13,6 m
Number of “building points” 413 (mean)
Courtyard closed areas Yes
Table 6: Summary of simulated LIDAR data.
Processing such datasets by SFS (Figures 3+5 relate to Plan-3)
has given very satisfactory results: ground trend surface and
building/outlier have been well detected (see Table 7).
Correct
Correct within 5%
Detection of surface type
Coefficient estimation
Statistical errors on classification:
1% kind (false outlier)
2" kind (false ground)
p estimate
0,0%
1,7%
Correct within 10%
Table 7: SFS filtering of the simulated data: general results.
The performance of the SFS for classification can be
significantly validate by applying onto same datasets the
program TerraScan® (Soininen, 2003), a very well known
software for LIDAR data processing developed by Terrasolid
Ltd. A binary classification (ground/non-ground) was obtained
by suitably exploiting the following routines:
1. “Classify ground”: classifies ground points by iteratively
building a triangulated surface model.
2. “Low points”: classifies points that are lower than other
points in the vicinity. It is often used to search for possible
error points that are clearly below the ground.
199
3. “Below surface”: classifies points that are lower than other
neighbouring points in the source class. This routine was
run after ground classification to locate points that are
below the true ground surface.
4. “By height from ground”: classifies points that are located
within a given height range when compared with ground
point surface model.
Comparison among true, SFS and TerraScan classification
results is shown in Figure 8. As a general statement, we can say:
e SFS provides about 2% of errors of second statistical kind
(false ground), so that some outlier has not been detected;
e TerraScan? seems to commit more than 10% of first kind
errors (false outlier), so that many points were “rejected”,
although they belong to the ground (but noisy) surface.
1800
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1000 £
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200 :
Bi Ground points for TerraScan
o 41 T I = I. 3
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Figure 8: True vs. SFS vs. TerraScan classification of points.
5.2 Testing on really acquired data (city of Gorizia)
To evaluate LIDAR technology for DTM production, millions
of points were acquired in October 2003 over the city of Gorizia
with an airborne Optech? ALTM 3033 laser scanning system.
Data strips have been split into different sub-zones, in order to
avoid heavy computations with huge quantities of memory
storage, but anyway still being capable to test the efficiency of
the SFS method for real cases. General characteristics of sub-
zones are reported in Table 9.
Surface type Urban area
Data type First & Last pulse
Number of points (n) 15.000 (mean for sub-zone
Raw data (not grid) Yes
1 point/m* (mean)
Points sampling
15.000 m? (mean)
Dataset area
Az 44,3 m
Vegetation Yes
Buildings Yes
Courtyard closed areas No
Table 9: Summary of Optech® LIDAR data on Gorizia.
The sub-zone submitted to test is the downtown square, mainly
constituted of quasi-horizontal plane terrain; furthermore
different types of building were present, together with high and
low vegetation and a lot of parked cars. No power-lines or other
structures were present.
LIDAR points were processed either by SFS or by TerraScan:
with this last software, firstly objects are classified in two
classes: ground and non-ground points. Successively, other
classes such as buildings and vegetation were detected yet.
The difference among SFS/TerraScan classifications regards
679 points (4,5% on 14.953 total points), ranked as “ground”
