Hybrid approach using deep learning and graph comparison for building change detection

Existing methods of detecting building changes from very-high-resolution (VHR) images are limited by positional displacement. Although various change detection (CD) methods including deep learning methods have been proposed, they are incapable of overcoming the aforementioned limitation. Therefore, this study proposes a two-step hybrid approach using deep learning and graph comparison to detect building changes in VHR temporal images. First, the building objects were detected using mask regional-convolutional neural networks (Mask R-CNN), wherein the centroid of the bounding box was extracted as the building node. Second, for each image, graphs were generated using the extracted building nodes. Accordingly, the changed nodes were identified based on iterative graph comparison, which could be voluntarily halted without setting thresholds by examining the changes in the proposed index while sequentially eliminating the building changes. To demonstrate the effectiveness of the proposed method, we experimentally tested the simulated images with synthetic changes and positional displacements. The results verified that the proposed method effectively reduced the false detections originating from positional inconsistencies. Consequently, the proposed method could overcome the limitations of conventional CD methods by employing a graph model based on the connectivity between adjacent buildings.