Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge

The updating of geodatabases (GDB) in urban environments is a difficult and expensive task. It may be facilitated by an automatic change detection method. Several methods have been developed for medium and low spatial resolution images. This study proposes a new method for change detection of buildings in urban environments from very high spatial resolution images (VHSR) and using existing digital cartographic data. The proposed methodology is composed of several stages. The existing knowledge on the buildings and the other urban objects are first modelled and saved in a knowledge base. Some change detection rules are defined at this stage. Then, the image is segmented. The parameters of segmentation are computed thanks to the integration between the image and the geodatabase. Thereafter, the segmented image is analyzed using the knowledge base to localize the segments where the change of building is likely to occur. The change detection rules are then applied on these segments to identify the segments that represent the changes of buildings. These changes represent the updates of buildings to be added to the geodatabase. The data used in this research concern the city of Sherbrooke (Quebec, Canada) and the city of Rabat (Morocco). For Sherbrooke, we used an Ikonos image acquired in October 2006 and a GDB at the scale of 1:20,000. For Rabat, a QuickBird image acquired in August 2006 has been used with a GDB at the scale of 1:10,000. The rate of good detection is 90%. The proposed method presents some limitations on the detection of the exact contours of the buildings. It could be improved by including a shape post-analysis of detected buildings. The proposed method could be integrated into a cartographic update process or as a method for the quality assessment of a geodatabase. It could be also be used to identify illegal building work or to monitor urban growth.