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AUTOMATIC OBJECT EXTRACTION FOR CHANGE DETECTION AND GIS UPDATE 
Stefan Hinz 
Remote Sensing Technology, Technische Universitaet Muenchen, 80 333 Muenchen, Germany 
Stefan.Hinz@bv.tu-muenchen.de 
KEY WORDS: Object Extraction, Image Understanding, Evaluation, Disaster Management 
ABSTRACT: 
In this paper we will revise the recent developments in the field of automatic object extraction for change detection and GIS update 
under different aspects: (1) level of automation; (2) available sensors and image characteristics; (3) effectiveness and processing 
time; and (4) verification/editing and quality of results. For most applications, none of these aspects can be considered isolated from 
the others. We will exemplify the importance of these aspects by the recent efforts made, in order to support the management and 
prevention of natural hazards and civil crises, e.g. caused by floodings or earthquakes. 
1. INTRODUCTION 
Aerial and satellite imagery plays a major role for the 
population and update of GeoDatabases, and many approaches 
have been developed to automate the involved processes to a 
large extent. Especially, semi- and fully automatic extraction of 
roads and buildings has gained much attention over the last 
decades. We will first give a short overview of recent 
developments in the field of automatic object extraction with 
focus on roads and buildings, before we put special emphasis on 
object extraction in the context of change detection and disaster 
management. We discuss the issues of object extraction under 
different aspects, in particular: 
• Level of automation 
• Available sensors and image characteristics 
• Effectiveness and processing time 
• Verification/editing and quality of results 
For most applications, none of these aspects can be considered 
isolated from the others. We will exemplify interaction and 
differing importance of these aspects by the recent efforts made 
to support the management and prevention of natural hazards 
and civil crises, e.g. caused by floodings or earthquakes. This 
application is in particular interesting since the importance of 
the above points varies over time before, during, and after such 
an event. 
2. AUTOMATIC OBJECT EXTRACTION - 
ISSUES AND DEVELOPMENTS 
The automatic extraction of objects like roads and buildings in 
natural environments is one of the challenging issues in photo- 
grammetry and computer vision. Natural environments are often 
characterized by a high complexity of the imaged scene. Fac 
tors having great influence are, for instance, the number of dif 
ferent objects, the amount of their interrelations, and the vari 
ability of both. Moreover, each factor - and thus the scene com 
plexity - is related to a particular scale. To accommodate for 
such factors, techniques like detailed modeling, (automatic) 
contextual reasoning, and internal evaluation of intermediate 
results have proven to be of great importance over the past 
years. It is clear that these techniques must be integral parts of 
an extraction system to attain reasonably good results over a 
variety of scenes. 
For the following discussion of recent work, we selected four 
important trends seen in object extraction over the last years and 
grouped the approaches accordingly: (1) Integration of multiple 
views; (2) Integration of functional and temporal characteristics; 
and (3) Integration of scale-space behavior. 
2.1 Integration of multiple cues 
Much recent work deals with integration and simultaneous 
processing of multiple cues for object extraction. Typical multi 
cue approaches rely on combination of: 
• different filters responses (textures, topographic sketch, 
• different spectral characteristics 
• different sensors (images, laser data, SAR, ...) 
• image information and external data (GIS) 
A major challenge for processing multi-cue data is the handling 
of uncertainties during object extraction. Cues derived from 
different sources should support and supplement each other. In 
practice, however, numerous conflicting cues appear in case of 
reasonably complex scenes. The derivation and utilization of 
confidence measures is thus a key issue to provide a basis for 
deciding which hypothesis may overrate another and which 
should be eliminated. 
An interesting earlier approach regarding the role of multiple- 
cue processing is given in (Tupin et al., 1999). They deal with 
finding consistent interpretations of SAR scenes (Synthetic 
Aperture RADAR). Different low level operators are applied to 
generate cues for one or more object classes (e.g. road and 
river), represented by a confidence value characterizing the 
match with the designated object classes. All confidence values 
are combined in an evidence-theoretical framework to assign 
unique semantics to each primitive attached with a certain 
probability. Finally, a feature adjacency graph is constructed in 
which global knowledge about interrelations between objects 
(road segments form a network, industrial areas are close to 
cities,...) is introduced in form of object adjacency probabilities. 
Based on the probabilities of objects and their relations the final 
scene interpretation is formulated as a graph labeling problem 
that is solved by energy minimization. 
For building extraction, cues are typically derived from 
overlapping images, high resolution surface models (DSMs) and 
GIS data. The DSM may stem from aerial laser scanner data, but 
also from matching aerial images with high overlap percentage. 
A straightforward approach for combining surface and cadastral 
data is given in (Durupt & Taillandier, 2006). Although 
supporting or conflicting hypotheses are not analyzed in very