248 citations · 499 across the 4 of their papers we have counts for
5 papers
C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images
Chengxi Han, Chen Wu, Meiqi Hu +2
A high-precision feature extraction model is crucial for change detection (CD). In the past, many deep learning-based supervised CD methods learned to recognize change feature patt…
Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery
Chengxi Han, Chen Wu, Haonan Guo +3
The rapid advancement of automated artificial intelligence algorithms and remote sensing instruments has benefited change detection (CD) tasks. However, there is still a lot of spa…
HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images
Chengxi Han, Chen Wu, Haonan Guo +2
Benefiting from the developments in deep learning technology, deep-learning-based algorithms employing automatic feature extraction have achieved remarkable performance on the chan…
HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection
Meiqi Hu, Chen Wu, Liangpei Zhang
The fast development of self-supervised learning lowers the bar learning feature representation from massive unlabeled data and has triggered a series of research on change detecti…
Binary Change Guided Hyperspectral Multiclass Change Detection
Meiqi Hu, Chen Wu, Bo Du +1
Characterized by tremendous spectral information, hyperspectral image is able to detect subtle changes and discriminate various change classes for change detection. The recent rese…