Building change detection for high-resolution remotely sensed images based on a semantic dependency

The change of buildings is one of the most valuable information in the monitoring of land use for urban areas. Change detection technique based on multitemporal remote sensing image is an effective approach to obtain information of feature change. However, with the continuous improvement of resolution in remote sensing image, conventional change detection methods have much difficulty in exactly extracting building changes. One difficulty is the displacement for buildings between the Multitemporal remote sensing images due to the different view angles of sensors, another difficulty is the shadows, the above-mentioned difficulties are highlighted in the high-resolution remotely sensed Images. In this paper, a novel method for the detection of building changes from high-resolution images in urban areas is proposed, the candidate changed areas are obtained base on the spectral difference, and then a semantic dependency relation is integrated by a morphological building index technique and a shadow detection method to identify the real changes. The proposed method is evaluated with a pair of QuickBird images of Qingdao City, China. Experimental results demonstrate that the proposed method have a better performance to extract the building changes.