Timur Murat Celikoyan 
  
AUTOMATIC EXTRACTION OF OBJECTS AND THEIR INTEGRATION INTO AN INFORMATION 
SYSTEM 
T. Murat CELIKOYAN and M. Orhan ALTAN 
Istanbul Technical University, Turkey 
Faculty of Civil Engineering, Division of Photogrammetry 
tikovant@sru. ins tu cducis . osltan@sty.ius. lu <à 
Working Group III/3 
KEY WORDS: Automation, GIS, Object Tracking 
ABSTRACT 
In photogrammetric practice, it takes a lot of time to measure continous objects manually. In order to gain time, many 
objects like roads and buildings can be extracted automatically. To extract these kind of objects, many attempts are 
done. The aim of this study is to develope a better algorithm for extraction. 
The extraction process can be divided into two groups. The first one is to obtain the borders of a geometrically closed 
object like a building, which can be defined as spatial objects, where the second one is to extract the unclosed objects 
like roads etc. These two groups seem likely, but there are some differences about the algoritms, which are used to 
obtain the borders of them. 
As application, several images are worked on. The first two examples are images of damaged buildings in Dinar 
Earthquake (1995) and Marmara Earthquake (1999), in which over 15.000 people were died and thousands of buildings 
were destroyed. 
For documentation and analysis of earthquake damages, an information systems is developed. As basic data, damaged 
buildings are evaluated photogrammetrically. By these applications, damages on buildings are extracted automatically. 
As further application, aerial images and remotely sensed data are worked on. Roads are extracted automatically on 
aerial images. As a last example, coast line of Marmara Region is extracted from remotely sensed data. 
1 INTRODUCTION 
In recent years, many approaches and studies are done in order to extract and evaluate of objects form digital images. 
Aims for these axtractions and evaluations were data acquisation for GIS as well as mapping. As basic data, aerial 
images and remotely sensed data from multiple resolution and structure were used. In low resolution images, only the 
road lines vegetation borders etc. can be extracted, whereby in high resolution images some profiles, road borders and 
buildings. 
For automatic road extraction Baumgartner et.al. (1999) has used aerial images from multiple scale and combined all 
the results in order to obtain a road network. Wiedemann and Hinz (1999) has extracted roads from multispectral 
satellite imagery. Further applications are made by Heipke and Straub (1999) in order to automatic updating a 
Geographical Information System using automatic extraction methods from multispectral satellite imagery. 
By automatic extraction, lots of approaches and applications can be given. What important is to finding the useful 
algorithm for the purpose. In this paper, another algorithm for extracting objects from terrestrial and aerial images is 
discussed and some examples are given. 
2 ALGORITHM FOR EXTRACTING GEOMETRICALLY NON-CLOSED OBJECT 
In the algorithm, pixel neighbourhoods are numbered as in Figure 1., in which having the same rotation in N, E, W and 
S. Regarding the problems studied before, it has been exposed that every circle must have the same neighbourhood as 
the neighbouring two circles. These neighbourhoods are chosen in the basic rotation as N, S, E, W. 
  
164 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000.