A systematic approach for building change detection using multi-source data

Abstract

Recent sensors give valuable data for remote sensing applications. Among these, building and change detection are important problems. Therefore, researchers worked on these problems using both 2D and 3D data. Some previous studies used only 2D data due to their availability. Yet others used either 3D data alone or 2D and 3D data in a joint manner. Besides, some studies only focused on building detection. Yet others used detected building information in change detection. In this study, we focus on 3D change detection based on building information. Therefore, we first detect buildings. At this step, we benefit from both 2D and 3D data. Then, we locate changes based on these detected buildings. We detect building pixels using panchromatic, multispectral, and Digital Surface Model (DSM) data using a decision tree classifier. Then, we refine the detected building pixels using morphological and shape based operations. Finally, we apply an object based hierarchical change detection method on the refined pixels. We tested our method on 780 buildings and obtained promising results.