Journal of Geo-information Science >
Multi-scale Cross Dual Attention Network for Building Change Detection in Remote Sensing Images
Received date: 2023-07-28
Revised date: 2023-10-06
Online published: 2023-12-05
Supported by
National Natural Science Foundation of China(61972414)
National Key Research and Development Program of China(2016YFC0303707)
Remote sensing image change detection is a crucial technique that utilizes remote sensing technology to analyze and compare image data captured at different time periods or scenes. In practice, features at varying scales encompass diverse representation ranges, enabling the extraction of more comprehensive and detailed information. This paper proposes a Multi-Scale Cross Dual Attention Network (MSCDAN) method for building change detection in remote sensing images using the multi-scale Cross Dual Attention (CDA) mechanism and residual convolution neural network architecture. The proposed method leverages the characteristics of a residual network to extract change features of different dimensions from remote sensing images. For each feature dimension, a CDA module is created, which utilizes both cross attention and dual attention mechanisms. It combines spatiotemporal information to capture time-series features of surface changes and identifies time-series related change patterns, such as periodic and persistent changes. In this way, the multi-scale CDA module enhances the correlation between different perspectives or feature maps within the input data, which facilitates the exchange and fusion of information in multiple dimensions and enhances the model capability for complex change scenes, leading to improved change detection performance. A Fully Transposed Convolutional Upsampling Module (FTCUM) is introduced to perform local feature fusion for each point in the feature map, and the change boundary is identified by the neural network. This avoids the problems of blurring and jaggedness brought by traditional methods like bilinear interpolation and allows for end-to-end training and optimization, making the method more effective in meeting the requirements of change detection tasks. Extensive experiments are conducted on two benchmark datasets, namely WHU-CD and DSIFN, to evaluate the performance of the proposed method. Compared to the mainstream method, i.e., DTCDSCN (Dual-Task Constrained Deep Siamese Convolutional Network), our proposed method increases the accuracy by 5.13% on the DSIFN dataset and by 1.3% on the WHU-CD dataset. Additionally, for other exiting methods, the proposed method is also better than the ChangeNet and LamboiseNet on the three datasets and outperforms the improved DeepLabv3+ and SRCD-Net on the CDD Dataset. These exceptional findings across various datasets confirm the effectiveness of the proposed method in detecting changes in remote sensing images. Through the application of residual networks and attention mechanisms, our approach achieves superior results in intricate scenarios. This study shows that our proposed method performs remarkably well on various datasets. It serves as a reference for further comprehensive research on remote sensing image change detection using multi-scale cross-pairwise attention networks.
ZHANG Jianbing , YAN Zexiao , MA Shufang . Multi-scale Cross Dual Attention Network for Building Change Detection in Remote Sensing Images[J]. Journal of Geo-information Science, 2023 , 25(12) : 2487 -2500 . DOI: 10.12082/dqxxkx.2023.230432
表1 不同方法在LEVIR-CD、WHU-CD和DSIFN数据集上的精确率(Pre.)、召回率(Rec.)、IoU、 分数与准确率(OA)对比Tab. 1 Comparison of Pre., Rec., IoU, score and OA of different methods on LEVIR-CD, WHU-CD and DSIFN dataset (%) |
模型 | LEVIR-CD | WHU-CD | DSIFN | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Pre. | Rec. | IoU | F1 | OA | Pre. | Rec. | IoU | F1 | OA | Pre. | Rec. | IoU | F1 | OA | ||||
ChangeNet | 69.81 | 50.15 | 68.71 | 78.30 | 96.62 | 81.60 | 58.66 | 72.15 | 83.45 | 97.51 | 78.16 | 85.69 | 71.45 | 81.75 | 86.20 | |||
LamboiseNet | 80.51 | 94.14 | 77.37 | 85.66 | 97.12 | 65.24 | 83.22 | 60.68 | 69.99 | 93.54 | 77.54 | 78.35 | 64.32 | 77.83 | 79.93 | |||
DTCDSCN | 88.53 | 86.83 | 78.05 | 87.67 | 98.77 | 63.92 | 82.30 | 56.19 | 71.95 | 97.42 | 53.87 | 77.99 | 46.76 | 63.72 | 84.91 | |||
MSCDAN | 89.68 | 84.49 | 86.17 | 89.30 | 98.81 | 88.82 | 81.41 | 77.52 | 84.95 | 98.69 | 85.61 | 84.37 | 78.66 | 84.99 | 89.27 |
表2 不同方法在CDD数据集上的精确率(Pre.)、召回率(Rec.)、IoU、 分数与准确率(OA)对比Tab. 2 Comparison of Pre., Rec., IoU, score and OA of different methods on CDD dataset (%) |
模型 | Pre. | Rec. | IoU | F1 | OA |
---|---|---|---|---|---|
改进Deep Labv3+ | 87.30 | 90.20 | — | 88.40 | 96.40 |
SRCD-Net | 92.55 | 93.34 | 86.81 | 92.94 | — |
MSCDAN | 93.68 | 92.80 | 88.74 | 93.18 | 97.92 |
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