In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 
55 
orthophotos were generated from the R, G, B and NIR channels 
(a true colour orthophoto was also generated) and the NDVI 
was calculated using the following formula: 
NDVI = (NIR-R) / (NIR+R) 
NDVI values range from -1 to +1 which suggests that if the 
pixel value is close to -1 it does not belong to healthy 
vegetation or vice versa. As a result, NDVI data could assist in 
separating vegetation from buildings in a DSM. 
Figure 3 shows the effect of sun position on selecting an NDVI 
threshold to separate buildings from vegetation. In the shadow 
area NDVI values are larger than the portion of the building 
directly facing the sun. A larger threshold value of 0.3 was 
selected to differentiate between buildings and vegetation. 
Because of this large threshold value some vegetation also 
appears with the buildings. 
NDVI Values 
Distance (m) 
Figure 3: NDVI Threshold for Buildings 
Within the LiDAR group of tasks, the first step is the 
generation of a DSM and DTM from the LiDAR data 
(TerraSolid software was used). In order to get the absolute 
height of the objects the DTM was subtracted from the DSM to 
give the NDSM. A further refinement of the NDSM can then be 
achieved by making use of multiple LiDAR echo data. These 
occur from building edges and trees. Figure 4 (Clode et al., 
2005) shows how the laser beam interacts with building edges 
and trees. 
Firstly, the filtered multiple echoes (figure 5) were converted 
into an image. Gaps between pixels of less than 3 metres were 
filled and a binary image was generated. Selecting a value for 
gap filling depends on the density of the original point data. If 
the density is high a small value can serve the purpose but it 
should not be too high that it causes individual trees close to 
each other to merge. 
The separation of multiple echo data (Figure 5) from the 
NDSM, by multiplication by the binary data, results in data 
only from those objects that record a single reflection. These 
include buildings and other solid objects but also vegetation 
that returned single echoes. 
\\ y* ; 
»*- 
„ 
/ f-n-" 
‘*¿0$ : " u 
. fa. ;r 
; * 
J v .. ' l * • * 
" ( - { 
'V 
. . .* 
r -*i” ' ~ • 
* 
■" 
* * 
o % 
. H 
* «. 
, ' 4' ' : 
.1? 
V * 
''' ' ¿V. J 
! 
<3/ > r >£*- 
' * r,*> V 
* • Vr •>,. 
• 
» « •'i 
' K. * 
+ <*■ 
. . t 
■* . ■— .•* 
* * • > 
& 
Figure 5: Filtered Multiple Echoes 
The next step was to apply a height threshold of 2.5m to the 
NDSM to eliminate objects such as hedges, cars etc and the 
resultant NDSM containing buildings, vegetation and other tall 
objects was converted to a binary image. All pixels having a 
value lower than or equal to 2.5 m were assigned a zero value 
and the remainder a value of one (Figure 6). 
A morphological operation such as closing and opening was 
used for filling small gaps in the binary image. Care should be 
taken as too many repetitions can result in rounding of the 
sharp building edges and loss of important detail. 
This binary image contains pixels that belong to buildings and 
remaining trees and needs further classification. This was 
achieved by introducing the NDVI image described as part of 
the image group of tasks. 
The NDSM and the NDVI images were combined and the 
maximum likelihood classification method was used for the