The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
361 
point data was included. In some rare cases in a single cell the 
presence of several data was noticed (up to 3 or 4 points) and so 
also an ambiguity. To solve this problem we used an algorithm 
which evaluated the mean height. Another possible solution 
could be the reduction of the cell size, but we preferred to avoid 
this choice that implicates a larger number of cells. 
The results of the rasterization process are 2 maps which 
correspond to Sardinia and GRASS classification. The 
advantage is the possibility to subtract the map (that is easy 
because the procedure is based on raster algebra operations) in 
order to obtain a raster map of the height differences. Where the 
both maps contained a float value the difference could be 
calculated, instead where one or both maps presented a “nodata” 
value also the difference map contained this one. 
This means that the statistics on the difference map are identical 
to the statistic carried out on the original correspondent vector 
points (exception made for the very few cells with an 
ambiguity). 
Starting from the original dataset a representative set was 
extracted considering the spatial distribution of the points and 
the morphology of the area. It is compose of 5 areas with a 
number of points variable from 1.5T0 6 to 8-10 6 . The 
morphology of the areas is also variable enough and includes 
urban, rural and wooded areas. 
Before filtering an outlier rejection was performed. The 
function used is v.outliers, which performs an interpolation of 
the data on a regular grid and then calculates the differences 
between the new surface and the spread points. The residuals 
are compared with a fixed threshold: data corresponding to 
residual exceeding the threshold are considered as outliers. 
The filtering method developed in GRASS needs some input 
parameters. This makes the algorithm highly customizable and 
capable to elaborate dataset morphologically different. In any 
case, a default parameter set is suggested as optimal being the 
choice of the parameter often complicated. 
The three functions that compose the algorithm were launched 
in sequence with the default set of parameters to perform a 
classification in 5 representative test areas, obtaining a 
subdivision of the points in terrain and object. Sardinia’s 
classification instead is based on terrain, vegetation and 
building points and considering that the points suitable for the 
realization of the DTM are the terrain points only, we compared 
the points classified as terrain by us and by TopScan. 
The statistics of the difference related to the points classified as 
terrain by both procedures are shown in table 1. 
AREA 
A 
B 
C 
D 
E 
F 
2856668 
2250790 
4005581 
728888 
1550308 
L 
2861842 
2253878 
4008225 
729011 
1550749 
TS 
1885229 
1655103 
3289329 
568432 
1214066 
GR 
2184190 
1830501 
3664729 
636919 
1408589 
C 
1676170 
1577317 
3233053 
525629 
1159922 
% TS/GR 
86,31 
90,42 
89,76 
89,25 
86,19 
% C / T 
88,91 
95,30 
98,29 
92,47 
95,54 
As it is possible to see the number of points classified as terrain 
by GRASS is always larger than TopScan, but the points in 
common between the two methods are approximately equal to 
TopScan’s terrain points. This means that GRASS finds the 
same points of TopScan but in addition finds others points. A 
following control demonstrated that the excess GRASS terrain 
points are generally located under the vegetation but on the 
ground (see. figure 1), and for this reason they were removed by 
the manual control of TopScan. That means that the considered 
points are not a classification error. 
Figure 1. Mismatch between GRASS and TopScan terrain 
points classification. 
In area B a building vector mask was manually performed 
starting from the orthophotos (177 buildings). Then the mask 
was rasterized with a resolution of 0.2 m. The basic idea was to 
use the mask to control the number of points classified as terrain 
that belong to the mask: these points are a classification error. 
The number of points classified as terrain but that lie in building 
mask is shown in the following table (Table 2). 
GRASS 
TopScan / Sardinia 
mask points 
percentage 
mask points 
percentage 
9819 
0.53% 
5588 
0.34% 
Table 2. Points classified as terrain that lie into buildings 
A followed analysis demonstrated that the points classified as 
terrain that lie in the mask are often close to the edges of the 
buildings. Figure 2 depicts a generic situation where it is 
possible to observe an error along the North edge of the roofs. 
For this reason we considered this points still belonging to the 
terrain, because they are due to errors committed during the 
manual realization of the mask. In fact the orthophotos present a 
perspective deformation due to the distance of the considered 
buildings from the nadir point and the objects that have a relief 
displacements are so distorted. 
Table 1. Difference between the classifications 
Legend: F, L = first and last pulses; TS, GR = TopScan and 
GRASS terrain points, C = corresponding points