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

Park, Seula; Song, Ahram

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

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딥러닝과 그래프 모델을 활용한 고해상도 영상의 건물 변화탐지

Type: Academic journal

Author: Park, Seula (Seoul National University) Song, Ahram (Kyungpook National University)

Journal: Korea Society of Surveying, Geodesy, Photogrammetry, and Cartography Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography Vol.40 No.3 KCI Accredited Journals SCOPUS

Published: 2022.6

Pages: 227 - 237 (11page)

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

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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.

#Change Detection #High Spatial Resolution Images #Graph Model #Deep Learning #Instance Segmentation #변화탐지 #고해상도 영상 #그래프 모델 #딥러닝 #인스턴스 분할

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Chen, J., Yuan, Z., Peng, J., Chen, L., Huang, H., Zhu, J., and Li, H. (2020), DASNet: Dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14, pp. 1194-1206. https://doi.org/10.48550/arXiv.2003.03608.

Chen, H., Zhang, K., Xiao, W., Sheng, Y., Cheng, L., Zhou, W., Wang, P., Su, Do., Ye, L., and Zhang, S. (2021), Building change detection in very high-resolution remote sensing image based on pseudo-orthorectification, International Journal of Remote Sensing, Vol. 42, No. 7, pp. 2686-2705. https://doi.org/10.1080/01431161.2020.1862437.

Deng, C., Yuan, X., Deng, L., and Chen, J. (2020), Detecting matching blunders of multi-source remote sensing images via graph theory, Sensors, Vol. 20, No. 13, 3712. https://doi.org/10.3390/s20133712.

Gong, J., Hu, X., Pang, S., and Wei, Y. (2019), Roof-cut guided localization for building change detection from imagery and footprint map, Photogrammetric Engineering and Remote Sensing, Vol. 85, No. 8, pp. 543-558. https://doi.org/10.14358/PERS.85.8.543.

He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017), Mask R-CNN, In Proceedings of the IEEE International Conference On Computer Vision, 22-29 October 2017, Honolulu, HI, USA, pp. 2980-2988. https://doi.org/10.1109/ICCV.2017.322.

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