The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
procedure in a way that it is possible to control the results of 
each single step. 
The algorithm is highly customizable, as it is based on a great 
number of parameters, even if, in its usage with datasets 
morphologically different, it has shown that a specific set of 
parameters can suggested as optimal. 
Starting from the points classified as single pulse terrain the 
DTM can be computed. The used command in this case (always 
performed by us and called v.surf.bspline, still free available in 
GRASS) interpolates the point data on a regular grid using 
bilinear or bicubic splines with Tychonov regularising 
parameter (Brovelli and Cannata, 2004). 
What we want to show in this paper is not the detailed 
functioning of the algorithm, but the quality of the products that 
can be obtained with it. 
The control was performed comparing our results (that from 
here we will call GRASS results) with the results obtained by 
TopScan, a German company which performed the same 
process on behalf of Sardinia Region by using a preliminary 
automatic algorithm followed by a manual control (hereafter 
those products are named Sardinia’s products). 
Sardinia’s products were checked by visual comparison with 
high resolution orthophotos (12.5 cm) and terrain measurements. 
For these reasons, we can assume Sardinia’s products as a good 
reference. 
The control regarded the classification of the raw data (filtering 
analysis), the DSM and the DTM. This kind of control can be 
considered as a relative control, because the initial data are the 
same for both two products, despite the two procedures are 
completely different and independent. 
To check the absolute precision of GRASS products a 
comparison with a new set of points measured with a GPS 
(RTK survey) was performed. In this case the data are 
completely independent and the larger accuracy of the GPS data 
(±0.03 m) than LiDAR (±0.2-K).3 m) ensures a good dataset as 
reference. 
The last control was to test the performances of the algorithm to 
verify the applicability on real cases. The computational cost 
depends on the number of the splines used to interpolate the raw 
data. A larger number of splines implicates a better resolution 
(but not always a better solution) but it increases the 
computational time. Thus a compromise between quality and 
time becomes necessary. 
In the following paragraphs the controls are presented, starting 
from the filtering and grid products, up to the algorithm 
performances. 
2. CLASSIFICATION CONTROL 
2.1 Dataset description 
The original dataset is compose on 286T0 6 points acquired with 
an Optech ALTM 3100. It covers an area of 59.3 km 2 along the 
East Coast of Sardinia Region, in the urbanized areas from 
Porto Rotondo to San Teodoro. It is composed of 63 strips 
acquired in three days, with a sidelap always larger than 50%. 
The altitude above the ground is 1000 m and the scan rate is 70 
kHz. The areas were mapped with a mean laser spot density of 
higher than 1 points/m 2 roughly. 
The dataset was filed as first and last pulses in ASCII text files, 
reporting the cartographic coordinates (UTM WGS84) and the 
intensity. From the ellipsoidal height the orthometric height was 
calculated according with Italian quasi-geoid. 
The whole dataset was divided in 28 areas, which corresponds 
to the municipalities along the considered part of Sardinia’s 
coast. 
2.2 Reference data description 
The raw data were acquired and processed by 
TopScan/HANSAER associated with the Italian company 
Aerosistemi S.r.l. and the German company Hansa Luftbild 
Sensorik und Photogrammetric GmbH. These companies 
performed the entire process on behalf of Sardinia Region. 
Firstly TopScan performed a classification of the raw data in 
three categories: ground, vegetation and buildings points. The 
used method is based on a preliminary automatic algorithm 
implemented by TopScan itself. This algorithm is able to divide 
the ground points from the object points. Then the object points 
were divided in buildings and vegetation points. Lastly a 
manual correction was performed to correct residual errors due 
to misclassifications. 
With the points classified as terrain a grid DTM was performed 
by using an interpolation. The DSM instead does not require 
any preliminary filtering operation and can be obtained with an 
interpolation of the data classified as first pulse. TopScan’s 
interpolation method was the linear prediction with bell curve as 
base function (Kraus and Pfeifer, 2001). 
Both DTM and DSM have a resolution of 2 m and were 
checked by visual comparison with high-resolution orthophotos 
and a spatial DB. The height accuracy instead was checked with 
survey measurements (GPS and Total Station). 
All these qualities make Sardinia’s products a good reference to 
analyze the classification and the DTM/DSM that our method is 
able to provide. 
2.3 Test of GRASS classification method 
To test the results of GRASS filtering algorithm a comparison 
with the Sardinia’s classification was performed. 
The main problem was to compare sparse points considering 
their classification. In fact a simple count of the points which 
belong to some determined categories is not sufficient to 
compare the classification (e.g. two methods can classify 
exactly the same number of points but these have a different 
location). That implicates that is necessary to compare each 
single point by using its spatial coordinates. 
The vast number of points (over 280T0 6 ) implicates more than 
10 17 combinations, and it makes impracticable the control itself. 
Even if we searched a method to decrease the number of 
operation, it could not make the control workable. 
For this reason a reduction of the data and a method able to 
speed up the control becomes inevitable. 
The method used to compare the vector points was based on 
their rasterization on a regular grid with square cells. The 
resolution was fixed equal to 0.5 m, so that into each cell only a 
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