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ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
USE DSM/DTM TO SUPPORT CHANGE DETECTION OF BUILDING IN URBAN AREA 
Hong FAN Jianqing ZHANG Zuxun ZHANG Zhifang LIU 
LIESMARS of Wuhan Univ., 129 Luoyu Road,Wuhan,China,430079 
Tel: 86-027-87881292, Fax:86-027-87643969,Email:fh@hp01 .wtusm.edu.cn 
Key Words: DSM , change detection, gradient direction analysis 
Abstract 
Timely change detection of urban area is very useful for the city's management, development and update of the urban geography 
information as well. Aerial Image has proven to be a valuable data source for these kind of application. As we know, the buildings are 
typically 3-dimendional real objects whose change will cause the change of height of Digital Surface Model (DSM for short). If we could 
make use of the "height" information to assist the detection of building change, it would be helpful for improvement of efficiency and 
performance of the automatic detection. 
This paper presented an new approach using DSM/DTM to support the change detection of man-made objects especially building in 
urban regions, which use both the height information and gray and texture information of building as one kind of data fusion technology 
to detect the change. In this paper, the corresponding methods and experiment results would be presented and analyzed in detail. 
1. PREFACE 
Rapid development of urban makes the data update more often 
than ever. On the other hand, manual handling of data update is 
a formidable task. An automatic or even semi-automatic way of 
data update will increase the speed of data update greatly. One 
of the most available and feasible approaches for detection 
automation is to utilize aerial images to explore changes of 
man-made features in urban area. 
Techniques of change detection have been widely used in 
change analysis of land use, monitoring of shifting cultivation, 
study of seasonal changes in pasture production, crop stress 
detection and other environmental change detection (Singh, 
1989), in the meantime, some methods were proposed by the 
previous references (Fung T, 1987) such as image difference, 
image regression, principal components analysis and 
background subtraction, most of which just use single image 
analysis such as gray information analysis to detect the 
changes. 
Up to now, for the change detection of man-made objects, such 
as buildings and roads, little research had been done in the field 
(Cushnie, 1989). Due to the limit of the spatial resolution satellite 
images are difficult to be applied in detection of building of urban 
area, aerial Image has proven to be an valuable data source for 
these kind of application. In this paper, a new approach of 
change detection of building based on aerial images was 
explored and introduced. 
As well known, changes of man-made objects of urban area are 
certainly 3-dimendional real objects' changes. Buildings are 
typically 3-dimendional real object whose change will cause the 
change of height of DSM. If we could make use of the height 
information to assist the detection of building change, it would 
contribute to raise the efficiency and performance of the 
detection. 
This paper presented a new approach to combine the height 
information and gray information of building as one kind of data 
fusion technology to detect the change of building in urban area. 
The corresponding theory and experiments were introduced and 
analyzed below. 
2. THE FUSION APPROACH OF DSM/DTM AND SINGLE 
IMAGE ANALYSIS 
The fusion approach of DSM/DTM and single image analysis 
synthetically make use of both the technique of stereo image 
analysis and of single image analysis such as gray and feature 
analysis, exactly speaking, it was a method that single image 
analysis was applied on the result of stereo image. It included 
the following steps, new and old DSMs were created 
automatically respectively by image matching firstly. By 
comparing the new and old DSMs the changed regions were 
extracted. After features were extracted from these regions, the 
straight lines were detected. Finally, the changed buildings and 
roads could be detected based on gradient direction analysis 
and recognition of line pattern of building between registered 
images.