结合Swin Transformer的多尺度遥感图像变化检测研究

doi:10.11996/JG.j.2095-302X.2024050941

图学学报 ›› 2024, Vol. 45 ›› Issue (5): 941-956.DOI: 10.11996/JG.j.2095-302X.2024050941

• 图像处理与计算机视觉 • 上一篇下一篇

结合Swin Transformer的多尺度遥感图像变化检测研究

刘丽¹^,²(), 张起凡¹^,²^,³, 白宇昂¹^,², 黄凯烨¹^,²

1.华北电力大学计算机系，河北保定 071051
2.河北省能源电力知识计算重点实验室，河北保定 071051
3.中国科学院空天信息创新研究院，北京 100080

收稿日期:2024-05-28 修回日期:2024-08-06 出版日期:2024-10-31 发布日期:2024-10-31
第一作者:刘丽(1978-)，女，副教授，博士。主要研究方向为人工智能和计算机视觉。E-mail：liuli@ncepu.edu.cn
基金资助:
河北省在读研究生创新能力培养资助项目(CXZZSS2024163);河北省重点研发项目(20310103D)

Research on multi-scale remote sensing image change detection using Swin Transformer

LIU Li¹^,²(), ZHANG Qifan¹^,²^,³, BAI Yuang¹^,², HUANG Kaiye¹^,²

1. Department of Computer Science, North China Electric Power University, Baoding Hebei 071051, China
2. Hebei Key Laboratory of Knowledge Computing for Energy & Power, Baoding Hebei 071051, China
3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100080, China

Received:2024-05-28 Revised:2024-08-06 Published:2024-10-31 Online:2024-10-31
First author：LIU Li (1978-), associate professor, Ph.D. Her main research interests cover artificial intelligence and computer vision. E-mail：liuli@ncepu.edu.cn
Supported by:
Hebei Province Graduate Student Innovation Ability Training Funding Project(CXZZSS2024163);The Key Research and Development Projects in Hebei Province(20310103D)

摘要/Abstract

摘要：

由于地物信息的复杂性及变化检测数据的多元性，遥感图像特征提取的充分性和有效性难以得到保证，导致变化检测方法获取的检测结果可靠性较低。虽然卷积神经网络(CNN)凭借有效提取语义特征的优势，被广泛应用于遥感领域的变化检测之中，但卷积操作固有的局部性导致感受野受限，无法捕获时空上的全局信息以至于特征空间对中远距离依赖关系的建模受限。为捕获远距离的语义依赖，提取深层全局语义特征，设计了一种基于Swin Transformer的多尺度特征融合网络SwinChangeNet。首先，SwinChangeNet采用孪生的多级Swin Transformer特征编码器进行远距离上下文建模；其次，编码器中引入特征差异提取模块，计算不同尺度下变化前后的多级特征差异，再通过自适应融合层将多尺度特征图进行融合；最后，引入残差连接和通道注意力机制对融合后的特征信息进行解码，从而生成完整准确的变化图。在CDD和CD_Data_GZ 2个公开数据集上分别与7种经典和前沿变化检测方法进行比较，CDD数据集中本文模型的性能最优，相比于性能第二的模型，F1分数提高了1.11%，精确率提高了2.38%。CD_Data_GZ数据集中本文模型的性能最优，相比于性能第二的模型，F1分数、精确率和召回率分别提高了4.78%，4.32%，4.09%，提升幅度较大。对比实验结果证明了该模型具有更好的检测效果。在消融实验中也证实了模型中各个改进模块的稳定性和有效性。本文模型针对遥感图像变化检测任务，引入了Swin Transformer结构，使网络可以对遥感图像的局部特征和全局特征进行更有效地编码，让检测结果更加准确，同时保证网络在地物要素种类繁多的数据集上容易收敛。

关键词: 变化检测, 孪生网络, Swin Transformer, 多尺度特征融合, 注意力机制, 特征差异提取

Abstract:

Due to the complexity of terrain information and the diversity of change detection data, it is difficult to ensure the adequacy and effectiveness of feature extraction in remote sensing images, resulting in low reliability of detection results obtained by change detection methods. Although convolutional neural networks are widely applied in remote sensing change detection due to their advantage of effectively extracting semantic features, the inherent locality of convolutional operations limits the receptive field, making it difficult to capture global spatiotemporal information, thus limiting the modeling of long-range dependencies in the feature space. To capture long-distance semantic dependencies and extract deep global semantic features, a multi-scale feature fusion network SwinChangeNet based on the Swin Transformer was designed. Firstly, SwinChangeNet utilized a twin multi-level Swin Transformer feature encoder for long-range context modeling. Secondly, a feature difference extraction module was introduced into the encoder to calculate the multi-level feature differences before and after changes at different scales, and then the multi-scale feature maps were fused through an adaptive fusion layer. Finally, residual connections and channel attention mechanisms were introduced to decode the fused feature information, thereby generating a complete and accurate change map. Compared with seven classic and cutting-edge change detection methods on two publicly available datasets, CDD and CD-Data_GZ, the proposed model demonstrated the best performance in both datasets. In the CDD dataset, compared with the second-best performing model, the F1 score increased by 1.11% and the accuracy by 2.38%. The proposed model outperformed the others in the CD-Data_GZ dataset. Compared to the second best-performing model, the F1 score, accuracy, and recall increased by 4.78%, 4.32%, and 4.09%, respectively, showing significant improvements. The comparative experimental results demonstrated that the proposed model has superior detection performance. The stability and effectiveness of each improved module in the model were also validated through the ablation experiment. In conclusion, the model proposed in this article focused on the task of remote sensing image change detection, introducing the Swin Transformer structure. This enabled the network to more effectively encode local and global features of remote sensing images, resulting in more accurate detection results, while ensuring that the network converges efficiently on datasets with a wide variety of land features.

Key words: change detection, siamese network, Swin Transformer, multi-scale feature fusion, attention mechanism, feature difference extraction

中图分类号:

TP751

刘丽, 张起凡, 白宇昂, 黄凯烨. 结合Swin Transformer的多尺度遥感图像变化检测研究[J]. 图学学报, 2024, 45(5): 941-956.

LIU Li, ZHANG Qifan, BAI Yuang, HUANG Kaiye. Research on multi-scale remote sensing image change detection using Swin Transformer[J]. Journal of Graphics, 2024, 45(5): 941-956.

图/表 28

图1 变化检测基本流程图

Fig. 1 Basic flowchart of change detection

图2 Swin Transformer结构图

Fig. 2 Swin Transformer structure diagram

图3 Swin Transformer Block结构图

Fig. 3 Swin Transformer Block structure diagram

图4 注意力计算窗口划分((a) W-MSA窗口划分；(b) SW-MSA窗口划分)

Fig. 4 Attention calculation window division ((a) W-MSA window division; (b) SW-MSA window division)

图5 滑动窗口操作

Fig. 5 Sliding window operation

图6 SwinChangeNet网络结构图

Fig. 6 SwinChangeNet network architecture diagram

图7 Swin Transformer编码器结构图

Fig. 7 Swin Transformer encoder structure diagram

图8 孪生网络结构

Fig. 8 Siamese network structure

图9 Patch Merging示意图

Fig. 9 Patch Merging diagram

图10 融合模块结构图

Fig. 10 Fusion module structure diagram

图11 轻量级注意力解码器结构

Fig. 11 Lightweight attention decoder architecture

图12 CBAM注意力模块结构

Fig. 12 CBAM attention module structure

图13 通道注意力模块结构

Fig. 13 Channel attention module structure

图14 空间注意力模块结构

Fig. 14 Spatial attention module structure

图15 残差模块结构

Fig. 15 Residual module structure

图16 CDD数据集裁剪示例

Fig. 16 CDD dataset cropping example ((a) T1; (b) T2; (c) Label)

图17 CD_Data_GZ数据集裁剪示例

Fig. 17 CD_Data_GZ dataset cropping example ((a) T1; (b) T2; (c) Label)

表1 实验评估指标信息

Table 1 Experimental evaluation indicator information

评价指标	公式	含义
总体精度 (OA)	$O A = T P + F N T P + T N + F P + F N$	正确分类的像素在总像素中占比
精确率 (P)	$P = T P T P + F P$	正确分类的像素在总正类中占比
F1分数 (F1)	$F 1 = 2 P R P + R$	评价模型综合性能
召回率 (R)	$R = T P T P + F N$	正确分类的像素在正类中占比

表1 实验评估指标信息

Table 1 Experimental evaluation indicator information

评价指标	公式	含义
总体精度 (OA)	$O A = T P + F N T P + T N + F P + F N$	正确分类的像素在总像素中占比
精确率 (P)	$P = T P T P + F P$	正确分类的像素在总正类中占比
F1分数 (F1)	$F 1 = 2 P R P + R$	评价模型综合性能
召回率 (R)	$R = T P T P + F N$	正确分类的像素在正类中占比

表2 CDD数据集对比实验结果/%

Table 2 Comparative experimental results of CDD dataset/%

Method	OA	P	F1	R
FC-EF	93.67	79.52	69.89	62.35
FC-Siam-conc	95.64	89.26	79.48	71.64
FC-Siam-diff	95.32	89.34	77.57	68.54
BIT	98.70	95.34	94.44	93.56
USSFC-NET	98.60	96.43	94.49	92.64
GCD-DDPM	98.86	94.76	94.93	95.10
DASUNet	98.71	94.93	94.32	93.71
SwinChangeNet	98.97	97.14	96.04	94.97

图18 CDD数据集的可视化分析结果((a) T1时刻图像；(b) T2时刻图像；(c)标签；(d) FC-EF；(e) FC-Siam-conv；(f) FC-Siam-diff；(g) BIT；(h) USSFC-NET；(i) GCD-DDPM；(j) DASUNet；(k) SwinChangeNet)

Fig. 18 Visualization analysis results of CDD dataset ((a) T1 moment image; (b) T2 moment image; (c) Label; (d) FC-EF; (e) FC-Siam-conv; (f) FC-Siam-diff; (g) BIT; (h) USSFC-NET; (i) GCD-DDPM; (j) DASUNet; (k) SwinChangeNet)

表3 CD_Data_GZ数据集对比实验结果/%

Table 3 Comparative experimental results of CD_Data_GZ dataset/%

Method	OA	P	F1	R
FC-EF	97.90	85.60	76.98	69.94
FC-Siam-conc	97.92	81.06	78.68	76.44
FC-Siam-diff	97.77	80.82	76.63	72.85
BIT	98.27	84.64	82.29	80.07
USSFC-NET	98.17	82.97	81.44	79.96
GCD-DDPM	97.09	87.92	85.26	83.86
DASUNet	96.93	86.71	83.23	77.97
SwinChangeNet	99.04	92.24	90.04	87.95

图19 CD_Data_GZ数据集的可视化分析结果((a) T1时刻图像；(b) T2时刻图像；(c)标签；(d) FC-EF；(e) FC-Siam-conv；(f) FC-Siam-diff；(g) BIT；(h) USSFC-NET；(i) GCD-DDPM；(j) DASUNet；(k) SwinChangeNet)

Fig. 19 Visualization analysis results of CD_Data_GZ dataset ((a) T1 moment image; (b) T2 moment image; (c) Label; (d) FC-EF; (e) FC-Siam-conv; (f) FC-Siam-diff; (g) BIT; (h) USSFC-NET; (i) GCD-DDPM; (j) DASUNet; (k) SwinChangeNet)

表4 CDD数据集消融实验结果

Table 4 Results of ablation experiments on the CDD dataset

模型结构	ST编码器	特征差异提取	CBAM	OA/%	P/%	F1-score/%	R/%
A	×	√	√	98.48	96.19	94.01	91.93
B	√	×	√	98.88	95.84	95.61	94.37
C	√	√	×	98.99	95.89	95.72	95.56
SwinChangeNet	√	√	√	98.97	97.14	96.04	94.97

图20 CDD数据集消融实验可视化结果((a) T1时刻图像；(b) T2时刻图像；(c)标签；(d)模型A；(e)模型B；(f)模型C；(g) SwinChangeNet)

Fig. 20 Visualization results of CDD dataset ablation experiments ((a) T1 moment image; (b) T2 moment image; (c) Label; (d) Model A; (e) Model B; (f) Model C; (g) SwinChangeNet)

表5 CD_Data_GZ数据集消融实验结果

Table 5 Results of ablation experiments on the CD_Data_GZ dataset

模型结构	ST编码器	特征差异提取	CBAM	OA/%	P/%	F1-score/%	R/%
A	×	√	√	98.07	81.36	80.59	79.83
B	√	×	√	98.83	92.91	87.52	82.72
C	√	√	×	98.94	91.16	89.11	87.15
SwinChangeNet	√	√	√	99.04	92.24	90.04	87.95

图21 CD_Data_GZ数据集消融实验可视化结果((a) T1时刻图像；(b) T2时刻图像；(c)标签；(d)模型A；(e)模型B；(f)模型C；(g) SwinChangeNet)

Fig. 21 Visualization results of CD_Data_GZ dataset ablation experiments ((a) T1 moment image; (b) T2 moment image; (c) Label; (d) Model A; (e) Model B; (f) Model C; (g) SwinChangeNet)

图22 训练过程损失函数变化情况

Fig. 22 Changes in the loss function during the training process ((a) FC-Siam-diff; (b) BIT; (c) USSFC-NET; (d) GCD-DDPM; (e) DASUNet; (f) SwinChangeNet)

图23 SwinChangeNet训练过程图

Fig. 23 SwinChangeNet training process diagram

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结合Swin Transformer的多尺度遥感图像变化检测研究

Research on multi-scale remote sensing image change detection using Swin Transformer

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