1 |
Zhang, Y., L, Fu, Y. Li and Y. Zhang, 2021. HDFNet: Hierarchical dynamic fusion network for change detection in optical aerial images, Remote Sensing, 13(8): 1440.
DOI
|
2 |
Tian, S., A. Ma, X. Zheng, and Y. Zhong, 2020. HiUCD: A large-scale dataset for urban semantic change detection in remote sensing imagery, Computer Vision and Pattern Recognition; Image and Video Processing, arXiv: 2011.03247.
|
3 |
Zhuang, F., Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He, 2020. A comprehensive survey on transfer learning, Proceedings of the IEEE, 109(1): 43-76.
DOI
|
4 |
Volpi, M., D. Tuia, F. Bovolo, M. Kanevski, and L. Bruzzone, 2013. Supervised change detection in VHR images using contextual information and support vector machines, International Journal of Applied Earth Observation and Geoinformation, 20: 77-85.
DOI
|
5 |
Wang, D., X. Chen, M. Jiang, S. Du, B. Xu, and J. Wang, 2021. ADS-Net: An attention-based deeply supervised network for remote sensing image change detection, International Journal of Applied Earth Observation and Geoinformation, 101: 102348.
DOI
|
6 |
Yang, K., G. Xia, Z. Liu, B. Du, W. Yang, M. Pelillo, and L. Zhang, 2020. Semantic change detection with asymmetric siamese networks, Computer Vision and Pattern Recognition, arXiv: 2010. 05687.
|
7 |
Vakili, M., K. Mohammad, and R. Masoumeh, 2020. Performance analysis and comparison of machine and deep learning algorithms for IoT data classification, Machine Learning arXiv: 2001.09636.
|
8 |
He, K., X. Zhang, S. Ren, and J. Sun, 2015. Deep residual learning for image recognition, Computer Vision and Pattern Recognition, arXiv: 1512.03385.
|
9 |
Chen, H. and Z. Shi, 2020. A spatial-temporal attention-based method and new dataset for remote sensing image change detection, Remote Sensing, 12(10): 1662.
DOI
|
10 |
Chung, S. and M. Chung, 2019. Pedestrian classification using CNN's deep features and transfer learning, Journal of Internet Computing and Services, 20(4): 91-102 (in Korean with English abstract).
DOI
|
11 |
Hussain, M., D. Chen, A. Cheng, H. Wei, and D. Stanely, 2013. Change detection from remotely sensed images: from pixel-based to object-based approaches, ISPRS Journal of Photogrammetry and Remote Sensing, 80: 91-106.
DOI
|
12 |
Jason, Y., J. Clune, Y. Bengio, and H. Lipson, 2014. How transferable are features in deep neural networks?, Advances in Neural Information Processing Systems, 27: 3320-3328.
|
13 |
Wang, J., K. Sun, T. Cheng, B. Jiang, C. Deng, Y. Zhao, D. Liu, Y. Mu, M. Tan, X. Wang, W. Liu, and B. Xiao, 2020. Deep high-resolution representation learning for visual recognition, IEEE Transaction on Pattern Analysis and Machine Intelligence, 1: 1-1.
DOI
|
14 |
Ji, S., S. Wei, and M. Lu, 2018. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery dataset, IEEE Transactions on Geoscience and Remote Sensing, 57(1): 574-586.
DOI
|
15 |
Liu, Q., L. Liu, and Y. Wang, 2017. Unsupervised change for multispectral remote sensing images using random walks, Remote Sensing, 9(5): 438.
DOI
|
16 |
Mo, J., S. Seong, and J. Choi, 2021. Comparative evaluation of deep learning-based building extraction techniques using aerial images, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 39(3): 157-165 (in Korean with English abstract).
DOI
|
17 |
Tatbul, N., T. Lee, S. Zdonik, M. Alam, and G. Justin, 2018. Precision and recall for time series, Proc. of 32nd Conference on Neural Information Processing Systems, Montreal, CA, Dec. 3-8, p. 1803.03639.
|