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

14 Apr 2024  ·  Chengxi Han, Chen Wu, HaoNan Guo, Meiqi Hu, Hongruixuan Chen ·

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.

PDF Abstract

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Benchmark
Change Detection CDD Dataset (season-varying) HANet F1-Score 89.23 # 16
Precision 92.86 # 5
F1 89.23 # 5
Overall Accuracy 97.32 # 6
IoU 90.55 # 4
Recall 85.87 # 5
KC 87.70 # 4
Change Detection DSIFN-CD HANet F1 62.67 # 5
IoU 45.64 # 4
Overall Accuracy 85.76 # 4
Precision 56.52 # 2
Recall 70.33 # 4
KC 54.01 # 2
Change Detection GoogleGZ-CD HANet F1 75.28 # 1
Precision 78.58 # 4
Recall 72.25 # 4
Overal Accuracy 88.34 # 4
KC 67.67 # 4
IoU 60.36 # 4
Change Detection LEVIR+ HANet F1 77.56 # 4
Prcision 79.70 # 3
Recall 75.53 # 4
OA 98.22 # 4
KC 76.63 # 4
IoU 63.34 # 4
Change Detection LEVIR-CD HANet F1 90.28 # 16
IoU 82.27 # 11
Overall Accuracy 99.02 # 7
F1-score 90.28 # 4
Precision 91.21 # 5
Recall 89.36 # 6
Change Detection S2Looking HANet F1-Score 58.54 # 7
Precision 61.38 # 4
Recall 55.94 # 3
OA 99.04 # 4
KC 58.05 # 4
IoU 41.38 # 4
F1 58.54 # 3
Change Detection SYSU-CD HANet F1 77.41 # 1
Precision 78.71 # 3
Recall 76.14 # 2
OA 89.52 # 3
KC 70.59 # 4
IoU 63.14 # 4
Change Detection WHU-CD HANet F1 88.16 # 5
Overall Accuracy 99.16 # 5
Precision 88.30 # 4
Recall 88.01 # 4
KC 87.72 # 4
IoU 78.82 # 4

Methods