International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
AUTOMATIC CLASSIFICATION OF URBAN ENVIRONMENTS FOR DATABASE REVISION USING LIDAR AND 
COLOR AERIAL IMAGERY 
N. Haala, V. Walter 
Institute of Photogrammetry, University of Stuttgart, Geschwister-Scholl-Str. 24, Stuttgart, D-72110, Germany, 
Norbert.Haala@ifp.uni-stuttgart.de, Volker.Walter@ifp.uni-stuttgart.de 
KEYWORDS: Classification, Building Extraction, Data Fusion, Laser Scanning. 
ABSTRACT 
This article describes the combination of Digital Surface Models (DSM) resulting from airborne laser scanning and colour aerial 
imagery for landuse classification in urban environments. The laser DSM is used to obtain information on the local height above the 
terrain surface for each pixel. This information is used in a following step in order to separate objects higher than the terrain like 
buildings and trees from objects which are at terrain level, like streets and grass-covered areas. The basic idea of the proposed 
algorithm is to combine this geometric information with additional multispectral information provided from colour aerial imagery. 
Both types of information are integrated in a pixel-based classification, where the information on the local height above the terrain is 
integrated as an additional channel together with the spectral channels. It is demonstrated that, by this approach, the classification of 
urban scenes can be improved considerably compared to a scene labelling, where only one type of data is applied. 
1. INTRODUCTION 
Spectral information has been widely used as a data source for 
thematic mapping applications. Surface material information 
can be derived by traditional classification techniques from 
multispectral imagery and can for example be used for mapping 
of man-made structures and natural features in complex urban 
scenes. During the past years, numerous classification 
algorithms have been developed for thematic mapping 
applications. They can be divided into unsupervised and 
supervised approaches. During an unsupervised classification, 
pixels are grouped into different spectral (and textural) classes 
by clustering algorithms without using prior information. After 
clustering, the spectral classes have to be associated with the 
land cover classes by an operator. In a supervised classification 
two basic steps are carried out. First, in a training stage an 
operator digitises training areas that describe typical spectral 
and textural characteristics of the dataset. In the following 
classification stage, each pixel of the dataset is assigned to a 
land cover class. For this classification stage, a lot of different 
approaches such as minimum-distance, parallelepiped or 
maximum likelihood classification are available (Lillesand and 
Kiefer (1994)). 
The main problem during thematic mapping of man-made 
structures and natural features is the limited accuracy and 
reliability of the results, as well as the frequently arising 
difficulties to discriminate a sufficient number of object 
categories. A common goal during data acquisition in built-up 
areas is the detection of objects like streets and buildings. 
However, this can be difficult if only spectral information is 
used, since, for some areas, roofs and streets are built of very 
similar material. This complicates or even prevents the 
discrimination of these objects due to their similar reflectance. 
The same problem can arise, if trees and grass-covered areas 
have to be differentiated. 
Traditionally, multispectral data has been the dominating source 
of information for landuse classification. Usually, the input data 
for the classification are multispectral images. Optionally, 
textural patterns, which are derived from the original spectral 
data, can be included. Up to now, auxiliary information on the 
surface topography has been mainly used for the geometric 
correction of the spectral data by the generation of ortho 
images. Additionally, surface topography information has been 
used for the radiometric correction of the multispectral imagery. 
This can be necessary, especially in hilly or mountainous 
terrain, since spectral reflectance values are influenced by the 
inclination and orientation of the terrain surface. In contrast to 
this, in our approach height data is not only applied for 
correction of the multispectral data, but also integrated as a 
complementary data source of equally important information. 
During data acquisition in urban environments, trees and 
buildings are object classes of major interest since these objects 
are very relevant for many applications like visualisations or 
simulations of the propagation of electro-magnetic radiation. In 
order to separate these types of objects from their surrounding, 
information on the local height above terrain can be very 
valuable since trees and buildings are higher than then- 
surrounding, whereas other objects of interest like streets and 
grass-covered areas are at the terrain level. The required 
information can be derived at least approximately from a DSM, 
which for our application is acquired by airborne laser scanning. 
Based on this DSM, the local height above terrain of the visible 
surface is derived. The result consists of all objects rising above 
the terrain approximately put on a plane. This surface is 
combined with the multispectral bands during a standard 
classification in order to improve the extraction of the required 
object classes.