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 strategy on the basis of not adding
change information is proposed in this article 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 (HANet), which can integrate
multiscale features and refine detailed features. The main part of HANet is the
HAN module, which is a lightweight and effective self-attention mechanism.
Extensive experiments and ablation studies on two CDdatasets with extremely
unbalanced labels validate the effectiveness and efficiency of the proposed
method.

通过深度学习技术的发展，本文提出了一种基于渐进前景平衡采样策略的深度学习模型来解决变化检测任务中改变和不改变的像素之间不平衡的问题，并设计了一种层次化注意力网络（HANet），以提高检测性能。