1 |
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.
DOI
|
2 |
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.
DOI
|
3 |
Shen, L., Lu, Y., Chen, H., Wei, H., Xie, D., Yue, J., Chen, R., Lv, S., and Jiang, B. (2021), S2Looking: A Satellite side-looking dataset for building change detection, Remote Sensing, Vol. 13, No. 24, 5094. https://doi.org/10.3390/rs1010000.
DOI
|
4 |
Shin, D., Kim, T., Han, Y., Kim, S., and Park, J. (2019), Change detection of building demolition area using UAV, Korean Journal of Remote Sensing, Vol. 35, No. 5_2, pp. 819-829. (in Korean with English abstract) https://doi.org/10.7780/kjrs.2019.35.5.2.6.
DOI
|
5 |
Sun, Y., Zhang, X., Huang, J., Wang, H., and Xin, Q. (2020), Fine-grained building change detection from very high-spatial-resolution remote sensing images based on deep multitask learning, IEEE Geoscience and Remote Sensing Letters, Vol. 19, pp. 1-5. https://doi.org/10.1109/LGRS.2020.3018858.
DOI
|
6 |
Van Etten, A., Lindenbaum, D., and Bacastow, T. M. (2018), Spacenet: A remote sensing dataset and challenge series, arXiv preprint arXiv:1807.01232. https://doi.org/10.48550/arXiv.1807.01232.
DOI
|
7 |
Varol, B., Yilmaz, E. O., Maktav, D., Bayburt, S., and Gurdal, S. (2019), Detection of illegal constructions in urban cities: Comparing LIDAR data and stereo KOMPSAT-3 images with development plans, European Journal of Remote Sensing, Vol. 52, No. 1, pp. 335-344. https://doi.org/10.1080/22797254.2019.1604082.
DOI
|
8 |
Ren, S., He, K., Girshick, R., and Sun, J. (2015), Faster R-CNN: Towards real-time object detection with region proposal networks, Advances in neural information processing systems, Vol. 28. https://doi.org/10.48550/arXiv.1506.01497.
DOI
|
9 |
Wu, J., Li, B., Qin, Y., Ni, W., and Zhang, H. (2021), An object-based graph model for unsupervised change detection in high resolution remote sensing images, International Journal of Remote Sensing, Vol. 42, No. 16, pp. 6209-6227. https://doi.org/10.1080/01431161.2021.1937372.
DOI
|
10 |
Qin, R., Tian, J., and Reinartz, P. (2016), 3D change detection-approaches and applications, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 122, pp. 41-56. https://doi.org/10.1016/j.isprsjprs.2016.09.013.
DOI
|
11 |
Jiang, H., Hu, X., Li, K., Zhang, J., Gong, J., and Zhang, M. (2020), PGA-SiamNet: Pyramid feature-based attention-guided siamese network for remote sensing orthoimagery building change detection, Remote Sensing, Vol. 12, No. 3, 484. https://doi.org/10.3390/rs12030484.
DOI
|
12 |
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.
DOI
|
13 |
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.
DOI
|
14 |
He, K., Gkioxari, G., Dollar, 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.
DOI
|
15 |
Kim, J. and Yu, K. (2012), Automatic detection of the updating object by a real feature matching based on shape similarity, Journal of the Korean Society of Survey, Geodesy, Photogrammetry, and Cartography, Vol. 30, No. 1, pp.59-65. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2012.30.1.059.
DOI
|
16 |
Li, L., Wang, C., Zhang, H., Zhang, B., and Wu, F. (2019), Urban building change detection in SAR images using combined differential image and residual u-net network, Remote Sensing, Vol. 11, No. 9, 1091. https://doi.org/10.3390/rs11091091.
DOI
|
17 |
Mo, J. S., Seong, S. K., and Choi, J. W. (2021), Change detection of building objects in urban area by using transfer learning, Korean Journal of Remote Sensing, Vol. 37, No. 6_1, pp. 1685-1695. (in Korean with English abstract) https://doi.org/10.7780/kjrs.2021.37.6.1.16.
DOI
|
18 |
Peng, D., Zhang, Y., and Guan, H. (2019), End-to-end change detection for high resolution satellite images using improved UNet++, Remote Sensing, Vol. 11, No. 11, 1382. https://doi.org/10.3390/rs11111382.
DOI
|
19 |
Prathap, G., and Afanasyev, I. (2018), Deep learning approach for building detection in satellite multispectral imagery, International Conference on Intelligent Systems, 25-27 September, Wroclaw, Poland, pp. 461-465.
|