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http://dx.doi.org/10.7848/ksgpc.2022.40.3.227

Building change detection in high spatial resolution images using deep learning and graph model  

Park, Seula (Institute of Engineering Research, Seoul National University)
Song, Ahram (Department of Location-Based Information System, Kyungpook National University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.40, no.3, 2022 , pp. 227-237 More about this Journal
Abstract
The most critical factors for detecting changes in very high-resolution satellite images are building positional inconsistencies and relief displacements caused by satellite side-view. To resolve the above problems, additional processing using a digital elevation model and deep learning approach have been proposed. Unfortunately, these approaches are not sufficiently effective in solving these problems. This study proposed a change detection method that considers both positional and topology information of buildings. Mask R-CNN (Region-based Convolutional Neural Network) was trained on a SpaceNet building detection v2 dataset, and the central points of each building were extracted as building nodes. Then, triangulated irregular network graphs were created on building nodes from temporal images. To extract the area, where there is a structural difference between two graphs, a change index reflecting the similarity of the graphs and differences in the location of building nodes was proposed. Finally, newly changed or deleted buildings were detected by comparing the two graphs. Three pairs of test sites were selected to evaluate the proposed method's effectiveness, and the results showed that changed buildings were detected in the case of side-view satellite images with building positional inconsistencies.
Keywords
Change Detection; High Spatial Resolution Images; Graph Model; Deep Learning; Instance Segmentation;
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Times Cited By KSCI : 4  (Citation Analysis)
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