CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 
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the theoretical work on fully automated object extraction and 
practical applications of the same (Mayer 2008). Success in 
automatic object extraction will also help in determining 
changes that occur between noise surveys (5 years) by 
comparing the extracted objects at the different epochs and 
should speed up the updating process of the G1S database. 
1.2 Related Work 
LiDAR has been extensively used for the generation of both 
DSMs (Digital Surface Model) and DTMs (Digital Terrain 
Model). Different classification methods have been used for the 
classification of terrain and off terrain points (Sithole & 
Vosselman, 2003). Different approaches have been used for the 
detection and reconstruction of buildings from LiDAR data 
(Brunn & Weidner, 1997 and Clode et al., 2004). Haitao et al. 
(2007) used aerial images and LiDAR data for land cover 
classification based on SVM (Scalable Vector Machine). Haala 
& Brener (1999) also used the combination of multispectral 
imagery and LiDAR data for the extraction of buildings, trees 
and grass covered areas. Trees and grass covered areas were 
classified easily from the multispectral imagery but were found 
difficult to separate. Similarly, trees and buildings were 
separated using height differences between DSM and DTM. 
Both data sources were combined in order to identify the three 
classification types. Rottensteiner et al. (2004) classified land 
cover into four different classes namely, buildings, trees, grass 
lands, and bare soil. This was achieved by combining LiDAR 
data and multispectral images. Prior to performing building 
detection by data fusion based on the theory of Dempster- 
Shafer, the LiDAR data was pre-processed to generate a DTM. 
For the extraction of roads different information sources such 
as multispectral images from airborne and space borne sensors 
were used. Clode et al. (2007) used only LiDAR for road 
extraction. Despite encouraging results, there are still many 
fundamental questions to be answered for road extraction in 
urban areas (Mayer et al., 2008). 
2. METHOD 
The method under investigation is based on a workflow that 
identifies and classifies buildings, trees and other objects by 
fusing the information from LiDAR and aerial image data. 
This information includes the normalised digital surface model 
(NDSM) and multiple echoes from the LiDAR data together 
with Normalized Difference Vegetation Index (NDVI) data 
generated from the airborne imagery. The method is depicted 
in Figure 1. Three major task groups may be identified, namely 
the image group, LiDAR group and object extraction group. 
3. WORKFLOW 
Within the image group of tasks, the first step is to produce 
orthophotos for each spectral channel of the ADS40 sensor, i.e. 
Red (R), Green (G), Blue (B) & Near Infrared (NIR). For these 
orthophotos, the required DSM can be created relatively 
automatically using the panchromatic forward and backward 
image data captured by the ADS40 sensor. The effect of DSM 
quality on orthophoto generation is shown in Figure 2. The 
upper part of the figure shows a rectified building using a DSM 
generated by aerial images and the lower part shows the same 
building rectified using a DSM from LiDAR data. 
Figure 1 : Method Workflow 
Figure 2: Effect of DSM on Building Rectification 
In case of the building illustrated in Figure 2, a DSM created 
from LiDAR data with a resolution of 0.5 m (the lower 
example) provided sharper edges compared to that generated 
from the image DSM and was used in the generation of the 
orthophotos. As a prerequisite to this step the quality of the 
registration between the airborne imagery and the LiDAR data 
must be verified. The Nearest Neighbourhood method was 
used as a sampling method for orthophoto generation. Separate