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

ABSTRACT

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.

KEYWORDS:

• building change detection
• graph model
• Mask R-CNN
• very high-resolution imagery

Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea NRF), funded by the Ministry of Education (2022R1F1A1063254, 2021R1A6A3A01086427).

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data supporting the findings of this study are available from the corresponding author, Ahram Song, upon reasonable request. SpaceNet 2: Building Detection v2 can be downloaded https://spacenet.ai/spacenet-buildings-dataset-v2/

Additional information

Funding

The work was supported by the National Research Foundation of Korea [2022R1F1A1063254]