Multilevel Deformable Attention-Aggregated Networks for Change Detection in Bitemporal Remote Sensing Imagery
Published in IEEE Transactions on Geoscience and Remote Sensing, 2022
This paper proposes multilevel deformable attention-aggregated networks (MLDANets) to effectively learn long-range dependencies across multiple levels of bitemporal convolutional features for multiscale context aggregation.
Recommended citation: X. Zhang, W. Yu and M. -O. Pun, “Multilevel Deformable Attention-Aggregated Networks for Change Detection in Bitemporal Remote Sensing Imagery,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-18, 2022, Art no. 5621518, doi: 10.1109/TGRS.2022.3157721.