International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004 
  
metric information, and satellite imageries, due to their 
wealth of spectral information, generate spectral data. The 
second category of the data sets is the topographic UGD (1e. 
3D digital map vector data). 
These data sets provide reference information, whereas the 
aerial and the satellite images serve to generate the most 
recent information and changes. To facilitate the ACD 
operations, sets of pre-processing modules are initially 
applied to the input data. These are basically fundamental 
radiometric and geometric corrections of aerial and satellite 
imageries such as the grey scale filtering, the determination 
of the sensor attitude and altitude parameters and registration. 
More particularly, as the proposed workflow (Figure 1) 
indicates, the change detection process is conducted under: 
object identification, object extraction, object recognition and 
change detection phases. 
  
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Figure 1. Workflow of presented method for automatic 
object recognition and reconstruction 
3. EXPERIMENTS AND RESULTS 
The proposed automatic change detection methodology was 
tested on a 1:1000 scale digital map and a pan-sharpen 
IKONOS scene of the city of Rasht, Iran (Figure 2). The 
maps have been produced in 1994 from 1:4000 aerial 
photographs by National Cartographic Centre (NCC) of Iran. 
490 
The satellite imagery was acquired on May 28" 2001. During 
these seven years time lapse between the generated digital 
map data and the IKONOS image acquisition, considerable 
changes have occurred in the city (See Figure 2). 
  
  
  
  
Figure 2. 1:1000 planimetric map of the city of Rasht (a), 
corresponding IKONOS Pan-sharpen (b), corresponding 
aerial photo (c). 
For two object classes of building and tree, the preliminary 
membership functions for the structural, textural and spectral 
information (STS) components are defined based on the 
knowledge of an experienced photogrammetric operator. 
Where membership functions can initially not be defined 
with sufficient confidence, they are tuned and modified by 
the learning potentials of the neuro-fuzzy technique. For the 
initial training operations of the system, 200 samples as 
learning data set and 50 samples as the checking data set 
were selected. The more samples are used the more 
comprehensive the membership functions would be defined 
and hence more reliability for the recognition process. To test 
the adaptation potentials of the recognition process for the 
modification of the membership functions, 200 samples were 
selected so that a great variety of viewing appearances for the 
building and tree object classes are covered. Based on these 
preliminary training operations the adjusted membership 
functions were determined. To assess the capabilities, 
reliability and efficiency of the proposed ACD method a 
portion of an urban portion of this area is selected. The 
selected area is well suited for first experiments with the 
proposed ACD method and shows a significant complexity as 
regards the proximity of the objects. The results of our ACD 
strategy is presented in Figure 3. The visual inspection of the 
obtained results demonstrates the high capability of our 
strategy. 
  
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