2008 
983 
DESIGN AND DEVELOPMENT OF A NEW MODEL FOR AUTOMATIC CHANGE 
DETECTION OF BUILDINGS FROM AERIAL IMAGES 
Simintaj ziraksaz ,Hamid Ebadi 2 ,Farshid Famood Ahmadi 3 , saeed sadeghian 4 
simin_ziraksaz@yahoo.comM.Sc. student, E-mail: 1. 
ebadi@kntu.ac.ir Assistant Professor, E-mail:2. 
farshid_famood@yahoo.com 3.Ph.D student, E-mail: 
Department of Photogrammetry and Remote Sensing 
K.N. Toosi university,Faculty of Geodesy and Geomatics Engineering 
4.Ph.D, E-mail: sedeghian@ncc.neda.net.ir 
National Cartographic Center of Iran (NCC) 
Commission VI, WG VI/4 
KEY WORDS: Aerial photos, Building, Change detection, Gis, Matching, Spatial database 
ABSTRACT: 
One of the most important tools in order to model real world phenomena is Geographic Information System. Real world phenomena 
are changing continuously, so it is necessary to detect and introduce changes to system in order to update GIS databases and model 
real world correctly. Geometric stability and high spatial resolution of aerial images leads to detect changes precisely. It is necessary 
to consider that feature recognition and extraction processes are two important stages in all of the change detection methods in which 
accurate results in detecting geometrical changes depends on performance of theses two stages. Each of these stages needs to use 
image processing algorithms. On the other hand, these algorithms are complicated, time consuming and the performance of them 
depends on specific conditions such as image acquisition conditions and so on. Therefore using the methods with low dependency on 
image processing can reduce these problems and increase the accuracy and reliability of the results. To achieve this aim, the 
integration of aerial photos and GIS spatial databases is suggested. In this research, a new algorithm was designed and implemented 
for automatic change detection of buildings based on the development of least squared matching technique. The accuracy assessment 
showed that change percentage of the regions that the algorithm can detect their changes correctly is 70% and the precision of this 
novel approach to approximate extracted edges with the real ones is 0.45 pixels. 
1. INTRODUCTION 
In photogrammetry and remote sensing, matching can be 
defined as the establishment of the correspondence between 
various data sets (Geodaetischas seminar ss/2000). The 
matching problem is also referred to as the correspondence 
problem. The data sets can represent images, but also maps, or 
object models and GIS data. The first step of matching is 
primitive extraction. The distinction between different matching 
primitives is probably the most prominent difference between 
the various matching algorithms. The primitives fall into two 
broad categories: either windows composed of grey values or 
features extracted in each image a priori are used in the actual 
matching step. The resulting algorithms are usually called: Area 
based matching (ABM), and Feature based matching (FBM), 
respectively (Bohuslav, 2004). Besides of these two algorithms, 
the combination algorithm can be also considered. This 
algorithm combines ABM and FBM in order to take the 
advantages of these two algorithms (Gruen, 1985). 
Correlation coefficient and least squares matching (LSM) are 
two approaches used in ABM. Cross-correlation works fast and 
well, if patches to be matched contain enough signal without too 
much high frequency content (noise) and if geometrical and 
radiometric distortions are kept at minimum. Both conditions 
are often not encountered in or with aerial images, while LSM 
minimizes differences in grey values between the template and 
search image patches in an adjustment process where geometric 
and radiometric corrections of one of matching windows are 
determined (Bohuslav, 2004). On the other hand FBM is 
implemented in three ways point-based matching edge-based 
matching and region-based matching based on the type of 
features extracted from images (Geodaetischas seminar 
ss/2000). Among of these approaches edge-based matching can 
presented accurate and reliable results because of its specific 
radiometric characteristics (Agouris, 1992), (Baltsavias, 1991). 
So the combination of LSM and edge-based matching is used in 
this article as an optimal method to obtain desired results in 
building change detection process. 
The goal of our study is to develop LSM for the identification 
of changes in buildings outlines. In this paper change is 
extracted thorough comparisons of observations. The 
differences in general exposure conditions among two different 
images in two distinct time instances may affect substantially 
the performance of the above described matching method. In 
order to minimize the effects of variations on our solution, we 
have to allow edge pixels to influence the solution more than 
the rest of the template. This can be performed by manipulating 
the corresponding weight matrix P. 
Therefore our approach is based on the use of least squares 
template matching, where prior data are analyzed to provide 
template information. The product of such a process is the 
identification of changes in object outlines. Our work is 
innovative in its use of prior information to provide templates 
for matching, and in its analysis of template information to 
assign proper weights in the least squares solution. In this paper 
we present theoretical models and implementation 
considerations behind our approach for change detection. The 
steps of implementation of this proposed algorithm is described 
as below.