## 5 Conclusions  
The model in this paper is designed to focus on remote sensing image change detection. The model uses a dual-branch structure in the encoder to extract local features, a triple-branch structure in the decoder to extract more global contextual information, and a TA module to effectively fuse the left and right branches with the middle branch. We validated the performance of the DETDNet on the SYSU-CD, LEVIR-CD, and BCDD datasets. In the three datasets, our model reached the optimal value in the F1 score, OA, and IoU. Among them, in the BCDD dataset, our F1 score, OA, and IoU were 3.3%, 0.2%, and 5.56% higher than the second best method, respectively. In the LEVIR-CD dataset, our model outperformed the next best method by 0.82%, 0.06%, and 1.37%, respectively. In the SYSU-CD dataset, our model was 1.41%, 0.71%, and 1.98% higher than the second best method, respectively. The BCDD dataset mainly contains large sparse buildings, and the LEVIR-CD contains small dense buildings. However, both contain pseudo-changes, and the data volume is relatively small compared to the SYSU-CD. These two datasets test the model's ability to learn and explore potential relationships with a small amount of data. The SYSU-CD has a large amount of data but not high labeling accuracy, which tests the model's generalization ability. In addition to this, we conducted four sets of ablation experiments to prove the significance of each component in the model. Although the receptive field was increased by the MFA module, the maximum receptive field was 23 after calculation. Therefore, it can be seen that the global features cannot be fully obtained in the shallow layers. Based on this, our subsequent work will focus on using the Transformer or MLP to obtain the global features to achieve a higher performance.  
Conceptualization, C.Z.; methodology, J.Y. and C.Z.; software, C.Z.; validation, L.W., C.Z. and J.Y.; formal analysis, L.W.; resources, L.W.; data curation, C.Z.; writing--original draft preparation, C.Z.; writing--review and editing, L.W.; visualization, L.W.; supervision, L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China under Grant U1903213 and the Scientific and Technological Innovation 2030 Major Project under Grant 2022 ZD0115802.  
The BCDD, LEVIR-CD, and SYSU-CD datasets are openly available at [[http://gpcv.whu.edu.cn/data/building_dataset.html](http://gpcv.whu.edu.cn/data/building_dataset.html)]([http://gpcv.whu.edu.cn/data/building_dataset.html](http://gpcv.whu.edu.cn/data/building_dataset.html)) (accessed on 30 March 2023), [[https://justchenhao.github.io/LEVIR/](https://justchenhao.github.io/LEVIR/)]([https://justchenhao.github.io/LEVIR/](https://justchenhao.github.io/LEVIR/)) (accessed on 30 March 2023), [https://mail2 sysuetucn-my.sharepoint.com/personal/liumx23_mail2_sysu_edu_cn/_layouts/15/onedrive.aspx?ga=1](https://mail2 sysuetucn-my.sharepoint.com/personal/liumx23_mail2_sysu_edu_cn/_layouts/15/onedrive.aspx?ga=1) (accessed on 30 March 2023), respectively.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & convolutional neural network \\ RS & remote sensing \\ CD & change detection \\ FCNNs & fully convolutional neural networks \\ FC-EF & fully convolutional early fusion \\ FC-Siam-conc & fully convolutional siamese concatenation \\ \end{tabular}  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Branch of MFE** & **Precision** & **Recall** & **F1** & **IoU** & **OA** \\ \hline
1, 2, 3 & 93.98 & 89.27 & 91.57 & 84.45 & 99.23 \\
0, 2, 3 & 93.24 & 90.74 & 91.97 & 85.14 & 99.26 \\
0, 1, 3 & 92.87 & 91.74 & 92.30 & 85.70 & 99.28 \\
0, 1, 2 & 93.81 & 91.00 & 92.38 & 85.85 & 99.30 \\ MFE & 93.84 & 91.59 & 92.70 & 86.40 & 99.32 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Importance of each branch of the MFE.