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EXTRACTION OF BUILDINGS AND TREES USING IMAGES AND LIDAR DATA 
N. Demir*, D. Poli, E. Baltsavias 
ETH Zurich, Institute of Geodesy and Photogrammetry, Wolfgang Pauli Str. 15, CH-8093 Zurich, 
Switzerland-(demir, poli, manos)@geod.baug.ethz.ch 
Commission IV, WG IV/3 
KEY WORDS: DTMs/DSMs, Lidar Data Processing, Multispectral Classification, Image Matching, Information Fusion, Object 
Extraction, Buildings, Trees 
ABSTRACT: 
The automatic detection and 3D modeling of objects at airports is an important issue for the EU FP6 project PEGASE. PEGASE is 
a feasibility study of a new 24/7 navigation system, which could either replace or complement existing systems and would allow a 
three-dimensional truly autonomous aircraft landing and take-off primarily for airplanes and secondary for helicopters. In this work, 
we focus on the extraction of man-made structures, especially buildings, by combining information from aerial images and Lidar 
data. We applied four different methods on a dataset located at Zurich Airport, Switzerland. The first method is based on DSM/DTM 
comparison in combination with NDVI analysis; the second one is a supervised multispectral classification refined with height 
information from Lidar data. The third approach uses voids in Lidar DTM and NDVI classification, while the last method is based 
on the analysis of the vertical density of the raw Lidar DSM data. The accuracy of the building extraction process was evaluated by 
comparing the results with reference data and computing the percentage of data correctly extracted and the percentage of missing 
reference data. The results are reported and commented. 
1. INTRODUCTION 
Pegase is a EU FP6 project aiming at studying the feasibility of 
a new 24/7 navigation system, which could either replace or 
complement existing systems and would allow a three- 
dimensional truly autonomous aircraft landing and take-off 
primarily for airplanes and secondary for helicopters (Pegase 
Web, 2008). Within the project, the acquisition of a reliable 
geospatial reference database of the airports, and in particular 
the automatic extraction of buildings and obstacles at airports, 
has a critical role for aviation safety. Often, 3D information of 
airports is not available, is not accurate enough, not complete, 
or not updated. Thus, methods are needed for generation of 
accurate and complete 3D geodata with high degree of 
automation. Buildings and trees are considered as obstacles, so 
they should be correctly detected. There are several methods 
applied for this purpose, based on image and/or airborne Lidar 
data. In our approach, objects are detected and extracted in 
aerial images and Lidar data through a combination of 
multispectral image classification, DSMs and DTMs 
comparisons and density analysis of the raw Lidar point cloud. 
This paper will give a brief overview of the related work on this 
subject. Then, after the description of the test area at Zurich 
Airport, Switzerland, the strategy and algorithms will be 
presented and the results will be reported, compared and 
commented. 
2. LITERATURE REVIEW 
Aerial images and Lidar data are common sources for object 
extraction. Regarding image-based analysis, multispectral 
classification uses spectral properties of objects and tries to 
extract them by using supervised or unsupervised methods. 
Currently, there are commercial systems (e.g. ENVI, ERDAS, 
e-Cognition, IDRISI, etc.), which use expert classification 
algorithms such as rule-based, object-oriented and fuzzy 
approaches. In digital photogrammetry, features of objects are 
extracted using 3D information from image matching or 
DSM/DTM data, spectral, textural and other information 
sources. Henricsson and Baltsavias (1997) combine image 
matching and spectral properties for the extraction process. 
Rottensteiner (2001) uses a semi-automated approach based on 
feature-based matching techniques. In Weidner and Foerstner 
(1995), above-ground objects are estimated using a normalised 
DSM (nDSM) by subtracting a DTM, estimated by a 
morphological filtering of the DSM, from the original DSM. In 
general, the major difficulty using aerial images is the 
complexity and variability of objects and their form, especially 
in suburban and densely populated urban regions. 
Regarding Lidar, building and tree extraction is basically a 
filtering problem in the DSM (raw or interpolated) data. Some 
algorithms use raw data (Sohn and Dowman, 2002; Roggero, 
2001; Axelsson, 2001; Vosselman and Maas, 2001; Sithole, 
2001; Pfeifer et al., 1998), while some others use interpolated 
data (Elmqvist et al., 2001; Brovelli et al., 2002; Wack and 
Wimmer, 2002). The use of raw or interpolated data can 
influence the performance of the filtering, but also its speed 
being slower for raw data. The algorithms differ also in the 
number of points they use at a time. In addition, every filter 
makes an assumption about the structure of bare-earth points in 
a local neighbourhood. This assumption forms the concept of 
the filter. Often, segmentation is performed to find the clusters 
which delineate objects and not facets of objects. The mostly 
used segmentation methods are based on region-based methods, 
like in Brovelli et al. (2002), Crosilla et al. (2005), or use 
Hough transform (Tarsha-Kurdi et al., 2007). In Elmqvist et al. 
(2001), the terrain surface is estimated by using active contours. 
The performance of the algorithm is good for large objects and 
all types of vegetation but small objects can not be always 
extracted (Sithole and Vosselman, 2003). Sohn and Dowman 
Corresponding author.