Style transformation-based change detection using adversarial learning with object boundary constraints
Published in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021
This paper proposes an object-level boundary-preserving generative adversarial network (BPGAN) for style transformation-based CD of bi-temporal images.
Recommended citation: W. Yu, X. Zhang, M. -O. Pun and M. Liu, “A Hybrid Model-Based and Data-Driven Approach for Cloud Removal in Satellite Imagery Using Multi-Scale Distortion-Aware Networks,” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 7160-7163, doi: 10.1109/IGARSS47720.2021.9554963.