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

Abstract

Benefiting from the developments in deep learning technology,\ndeep-learning-based algorithms employing automatic feature extraction have\nachieved remarkable performance on the change detection (CD) task. However, the\nperformance of existing deep-learning-based CD methods is hindered by the\nimbalance between changed and unchanged pixels. To tackle this problem, a\nprogressive foreground-balanced sampling strategy on the basis of not adding\nchange information is proposed in this article to help the model accurately\nlearn the features of the changed pixels during the early training process and\nthereby improve detection performance.Furthermore, we design a discriminative\nSiamese network, hierarchical attention network (HANet), which can integrate\nmultiscale features and refine detailed features. The main part of HANet is the\nHAN module, which is a lightweight and effective self-attention mechanism.\nExtensive experiments and ablation studies on two CDdatasets with extremely\nunbalanced labels validate the effectiveness and efficiency of the proposed\nmethod.\n