Dense Hybrid Attention Network for Remote Sensing Building Change Detection

Qinglin Tian; Donghua Lu; Yao Li; Chengkai Pei

doi:10.3788/AOS241436

Acta Optica Sinica, Volume. 45, Issue 6, 0628008(2025)

Dense Hybrid Attention Network for Remote Sensing Building Change Detection

Qinglin Tian1,*... Donghua Lu1, Yao Li2 and Chengkai Pei1 |Show fewer author(s)

¹National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, Beijing Research Institute of Uranium Geology, Beijing 100029, China

²School of Geographical Sciences, Southwest University, Chongqing 400715, China

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Figures & Tables(13)

Fig. 1. Overview architecture of proposed DHANet

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Fig. 2. Convolutional block attention module

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Fig. 3. Diagrams of traditional convolution and dilated convolution. (a) Traditional convolution; (b) dilated convolution

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Fig. 4. Multi-scale features aggregation module

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Fig. 5. Hybrid attention module

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Fig. 6. Interlaced sparse self-attention module

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Fig. 7. Data sets. (a) LEVIR-CD; (b) WHU-CD

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Fig. 8. Qualitative results on LEVIR-CD dataset

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Fig. 9. Qualitative results on WHU-CD dataset

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Table 1. Quantitative results on LEVIR-CD dataset

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Table 1. Quantitative results on LEVIR-CD dataset

Method	P /%	R /%	F₁ /%	IoU /%
FC-EF	86.91	80.17	83.40	71.53
FC-Siam-Conc	91.99	76.77	83.69	71.96
FC-Siam-Diff	89.53	83.31	86.31	75.92
STANet	83.81	91.00	87.26	77.40
IFN	93.02	86.33	89.55	81.08
BIT	91.89	88.48	90.15	82.07
DHANet	92.02	90.08	91.04	83.55

Table 2. Quantitative results on WHU-CD dataset

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Table 2. Quantitative results on WHU-CD dataset

Method	P /%	R /%	F₁ /%	IoU /%
FC-EF	86.44	71.24	78.11	64.08
FC-Siam-Conc	87.55	75.10	80.85	67.85
FC-Siam-Diff	86.40	76.64	81.23	68.39
STANet	75.70	89.85	82.17	69.74
IFN	91.65	82.94	87.08	77.11
BIT	89.65	85.04	87.28	77.44
DHANet	90.86	87.91	89.36	80.77

Table 3. Quantitative results of ablation experiments
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Table 3. Quantitative results of ablation experiments
DC MSA HAM P /% R /% F₁ /% IoU /%
× × × 89.83 87.64 88.72 79.73
√ × √ 90.62 89.09 89.85 81.57
√ √ × 90.55 89.25 89.90 81.65
× √ √ 91.83 89.37 90.58 82.79
√ √ √ 92.02 90.08 91.04 83.55

Table 4. Comparisons of complexity on LEVIR-CD dataset
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Table 4. Comparisons of complexity on LEVIR-CD dataset
Method Params /M FLOPs /G F₁ /%
FC-EF 1.35 2.92 83.40
FC-Siam-Conc 1.54 4.55 83.69
FC-Siam-Diff 1.35 3.99 86.31
STANet 23.32 23.84 87.26
IFN 50.71 82.26 89.55
BIT 3.55 10.60 90.15
DHANet 21.65 32.26 91.04

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Qinglin Tian, Donghua Lu, Yao Li, Chengkai Pei. Dense Hybrid Attention Network for Remote Sensing Building Change Detection[J]. Acta Optica Sinica, 2025, 45(6): 0628008

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