# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 3 Results
### Comparison and Analysis
#### 3.2.1 The Performance of the Proposed Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1 Score** & **IOU** \\ \cline{2-5}
**Method** & & & **CDD** & \\ \hline FC-Siam-Diff & 0.7830 & 0.6260 & 0.6920 & 0.5335 \\ STANet & 0.8520 & 0.8920 & 0.8720 & 0.7723 \\ IFN & 0.9500 & 0.8610 & 0.9030 & 0.8237 \\ ChangeSTAR * & 0.9280 & 0.9132 & 0.9205 & 0.8527 \\ SNUNet-CD * & 0.9140 & 0.8420 & 0.8765 & 0.7802 \\ SiUNet3+CD & 0.9470 & 0.8700 & 0.9069 & 0.8296 \\ BIT * & **0.9506** & **0.9417** & **0.9461** & **0.8978** \\ ChangeFormer & 0.9363 & 0.8549 & 0.8938 & 0.8079 \\ Type I (ours) * & 0.9439 & 0.8972 & 0.9200 & 0.8517 \\ Type II (ours) * & 0.9290 & 0.8761 & 0.9017 & 0.8210 \\ Type III (ours) * & 0.9313 & 0.8823 & 0.9061 & 0.8284 \\ \hline
**Method** & & & & \\ \hline FC-Siam-Diff * & 0.7690 & 0.4857 & 0.5954 & 0.4239 \\ STANet * & 0.7903 & 0.7660 & 0.7780 & 0.6366 \\ IFN * & 0.4389 & 0.4580 & 0.4483 & 0.2889 \\ ChangeSTAR * & 0.8298 & 0.8202 & 0.8250 & 0.7021 \\ SNUNet-CD * & 0.7839 & 0.8062 & 0.7949 & 0.6596 \\ SiUNet3+CD * & 0.8009 & 0.7797 & 0.7901 & 0.6531 \\ BIT * & 0.8530 & 0.8047 & 0.8281 & 0.7067 \\ ChangeFormer & 0.8847 & 0.7686 & 0.8225 & 0.6986 \\ Type I (ours) * & **0.8851** & 0.8316 & **0.8575** & **0.7506** \\ Type II (ours) * & 0.8847 & 0.8294 & 0.8562 & 0.7485 \\ Type III (ours) * & 0.8710 & **0.8350** & 0.8526 & 0.7431 \\ \hline
**Method** & & & & \\ \hline FC-Siam-Diff ** & 0.7218 & 0.6501 & 0.6841 & 0.5198 \\ STANet * & 0.7868 & 0.1614 & 0.2679 & 0.1547 \\ IFN * & 0.4644 & 0.4859 & 0.4749 & 0.3114 \\ ChangeSTAR * & **0.6855** & 0.7001 & 0.7741 & 0.6314 \\ SNUNet-CD ** & 0.7708 & **0.7722** & 0.7715 & 0.6280 \\ SUUNet3+CD ** & 0.7725 & 0.7550 & 0.7637 & 0.6177 \\ BIT * & 0.7990 & 0.6316 & 0.7055 & 0.5450 \\ ChangeFormer * & 0.6934 & 0.5179 & 0.5929 & 0.4214 \\ Type I (ours) ** & 0.8020 & 0.7647 & 0.7829 & 0.6433 \\ Type II (ours) ** & 0.8216 & 0.7513 & **0.7849** & **0.6459** \\ Type III (ours) ** & 0.7857 & 0.7274 & 0.7554 & 0.6070 \\ \hline \hline \end{tabular} Best results are highlighted in bold font. The precision, recall, F1 score, and IOU are averaged over all images in the three testing datasets. * Means our re-implemented results and/or pretrained files. * Represents that we initialized the models using weights pretrained on the PIESAT-CD dataset.  
\end{table} 
Table 1: Performance comparison on CDD, PIESAT-CD, and CD_Data_GZ datasets. FC-Siam-Diff, STANet, and SNUNet-CD have the worst performance. The CD accuracies of our three proposed models, especially Type I STDecoder-CD, are very competitive: their F1 scores and IOUs are all above the average levels of 0.8853 and 0.7999 by a large margin, although there are no pretrained parameters involved in the training process. In addition, as illustrated in Figure 5, compared with the visualization results of the compared methods, our proposed STDecoder-CD series models successfully returns the changed areas with relatively complete shapes/boundaries, high object internal compactness, and fine details. In particular, they are able to identify each slightly changed object with a clear and separated boundary, whereas other methods, except for SiUNet3+CD, often clump several small changes together. Although ChangeSTAR and BIT have higher values for most of the quantitative metrics in Table 1, our models could show better visual performance in practical applications. On the PIESAT-CD dataset, our proposed methods achieve substantially higher accuracies when compared to others, including the FCN-based methods (FC-Siam-Diff, ChangeSTAR, SNUNet-CD, and SiUNet3+-CD), CNN + Attention-based methods (e.g., IFN, and STANet), CNN + transformer-based method (BIT), and pure transformer-based method (ChangeFormer). Although many of the PIESAT-CD image pairs are not accurately labeled, the F1 scores achieved by STDecoder-CD are still above the 85% level commonly recommended for industrial applications.