HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images

Benefiting from the developments in deep learning technology, deep learning-based algorithms employing automatic feature extraction have achieved remarkable performance on the change detection (CD) task. However, the performance of existing deep learning-based CD methods is hindered by the imbalance between changed and unchanged pixels. To tackle this problem, a progressive foreground-balanced sampling (PFBS) strategy on the basis of not adding change information is proposed to help the model accurately learn the features of the changed pixels during the early training process and thereby improve detection performance. Furthermore, we design a discriminative Siamese network, Hierarchical Attention Network…

• NN
  National Natural Science Foundation of China

  Awards: 62225113, 61971317, T2122014, 42230108
• NK
  National Key Research and Development Program of China

  Awards: 2022YFB3903405, 2022YFB3903300