A COMPARISON OF BAYESIAN AND EVIDENCE-BASED FUSION METHODS FOR 
AUTOMATED BUILDING DETECTION IN AERIAL DATA 
K. Khoshelham 3 ’ *, S. Nedkov 3 , C. Nardinocchi 3, b 
d Optical and Laser Remote Sensing, Delft University of Technology, 2629 HS Delft, The Netherlands 
- k.khoshelham@tudelft.nl 
b DITS, University of Rome “La Sapienza”, 00184 Roma, Italy - carla.nardinocchi@uniromal.it 
Commission VI, WG VI/4 
KEY WORDS: Fusion, Building detection, Automation, Aerial image, Laser scanning 
ABSTRACT: 
Automated approaches to building detection are of great importance in a number of different applications including map updating 
and monitoring of informal settlements. With the availability of multi-source aerial data in recent years, data fusion approaches to 
automated building detection have become more popular. In this paper, two data fusion methods, namely Bayesian and Dempster- 
Shafer, are evaluated for the detection of buildings in aerial image and laser range data, and their performances are compared. The 
results indicate that the Bayesian maximum likelihood method yields a higher detection rate, while the Dempster-Shafer method 
results in a lower false-positive rate. A comparison of the results in pixel level and object level reveals that both methods perform 
slightly better in object level. 
1. INTRODUCTION 
Automated approaches to building detection are of great 
importance in a number of applications, including map updating, 
city modeling and monitoring of informal settlements. Up-to- 
date maps are in high demand with the current widespread use 
of navigation systems. Map updating is a tedious task when it is 
performed manually by an operator. Each building has to be 
inspected in the map and in a recent aerial image (or a stereo 
pair). Changes are marked and the map is updated accordingly. 
This process is expensive and time consuming. Automation of 
this process will save time and cost, making it possible to do 
faster and more frequent map updating. 
Using data from only one source of data often does not provide 
enough information to correctly detect buildings in an 
automated fashion. By fusing data from multiple sources the 
chances of correctly detecting buildings increase. Several 
methods of data fusion have been used for the detection of 
buildings from multi-source aerial data (Bartels and Wei,2006; 
Lu et al.,2006; Rottensteiner et al.,2004a; Walter,2004). While 
relatively successful application of these methods has been 
reported, a comparison of the performance of the methods is not 
available. The objective of this paper is to provide a comparison 
of the two main data fusion methods, namely Bayesian and 
Dempster-Shafer, as applied to the detection of buildings from 
multi-source aerial data. 
The paper has the following outline: in the next section an 
overview of the previous research in the field of automated 
building detection is given. This includes building detection 
using one aerial image, stereo and multiple-overlap images, 
using height data and through the fusion of several sources. 
Section 3 provides a brief description of the Bayesian decision 
theory and the Dempster-Shafer evidence theory. In Section 4 
feature extraction and evidence gathering for classification and 
morphological post-processing for building detection are 
discussed. Experimental results and a comparison of the 
performance of the methods are presented in Section 5. 
Conclusions are drawn in Section 5. 
2. RELATED WORK 
Building detection from aerial images has been a hot topic since 
the early 1990’s. Early approaches were based on a single 
image. Buildings were detected by making use of their shadows 
(Lin and Nevada, 1996). Shadows have the disadvantage of not 
being visible in all situations. In addition, they can obscure one 
another thereby forming complex shapes, which do not 
resemble the original buildings. Lin and Nevatia,(1996) 
furthermore assume that the roofs of the buildings are 
rectilinear, flat, and that the shadows cast by the buildings fall 
on flat ground. This is clearly not the case in many urban areas. 
Using more than one image supplies more information and 
other methods of detection can be applied. Fischer et al.,( 1998) 
use multiple images to recognize building features (points, lines 
and regions). These features are then used to construct building 
comers, wings, and faces, which in turn are combined to 
reconstruct a building. Fradkin et al.,(2001) use multiple images 
to detect facades of buildings. Several images from different 
angles are taken so that the problem of occlusion is reduced to 
some extent. After the facade has been found the rest of the 
building is detected. Contrary to roof detection (Khoshelham et 
al.,2005; Muller and Zaum,2005), facades have less area from 
which they can be detected, making their detection troublesome. 
Using several images it is possible to construct a height model 
of the scene. Weidner and Forstner,(1995) and Brunn and 
Weidner,(1997) use the height differences between buildings 
and the ground to make a guess about what is a building and 
what is not. Using this method, trees can be classified as 
Corresponding author.