Building change detection based on 3D reconstruction

Automatic building change detection at different periods is very important for city monitoring, disaster assessment, map updating, etc. Some existing data sources could be used in this task such as 3D geometry model (e.g. Digital Surface Model, Geographic Information System) and radiometric images from satellites or special aircrafts. However, it is too expensive for timely change detection by using these above methods. With the rapid development of UAV technique, capturing the city building images with high resolution camera at a low altitude becomes cheaper and cheaper. Using these easily acquired aerial images, we proposed a novel change detection framework based on RGB-D map generated by 3D reconstruction, which can overcome the large illumination changes. Firstly, an image-based 3D reconstruction is applied to retrieve two point clouds and related camera poses from two aerial image sets captured at different periods. Then an RGB-D map could be generated from each 3D model, followed by a 2D-3D registration procedure to align the two reconstructed 3D point clouds together. At last, a difference depth map could be generated and from which we can use random forest classification and component connectivity analysis techniques to segment the changed building areas out. Experimental results have illustrated the effectiveness and applicability of the proposed framework.