AUTOMATIC CHANGE DETECTION OF GEOSPATIAL DATABASES BASED ON A DECISION-LEVEL FUSION TECHNIQUE

Monitoring of changes in topographic urban geospatial databases (UGDs) is one of the main requirements of urban planners, urban decision-makers and managers. In this paper, an attempt has been made to design an automatic change detection (ACD) solution. The approach presented here takes advantage of fusion of descriptive and logical information represented on two levels. That is, information fusion to exploit the multi-level characteristics of the objects and logic fusion for enhancing the learning and with this the recognition abilities of the system. The proposed ACD process utilizes two types of data sets. Firstly, aerial and satellite images are used as data sources for the generation of a Digital Surface Model as well as for extracting textural and spectral information. Aerial images, because of their geometric stability, provide metric information, and satellite imageries, due to their wealth of spectral information, generate spectral data. The second type of data is the topographic urban geospatial databases. These data sets provide reference information, whereas the aerial and the satellite images serve to generate the more recent information and changes. The change detection process includes object identification, object extraction, object recognition and change detection phases. The proposed automatic change detection methodology was tested on a 1:1000 scale digital map of the city of Rasht in Iran by using of 1:4000 aerial photos and a pan-sharpened IKONOS scene. Visual inspection of the obtained results demonstrates the high capability of the proposed method