# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 4 Discussion  
A good design of a decoder plays an important but often overlooked role in improving the final change detection performance. However, efforts to investigate the effect of different decoding strategies are unexplored in the current literature. This is a challenging topic that warrants separate studies. Different from existing methods, STDecoder-CD pays particular attention to the significance of the decoder, and in our opinion, there are several meaningful aspects worth discussing, which are listed in the following paragraphs. First, at the center of STDecoder-CD is the powerful change feature extractor: TranscNN. As shown in Figure 1, because of the patch embedding operation, the maximum size of the hierarchical feature maps is only H/4 times W/4--a quarter of the original image size (H \(\times\) W)--which implies that there is a significant loss of spatial information for subsequent decoding and classification processes. However, TransCNN still has a certain capability to trace small changes and narrow-shaped objects, as shown in Figures 5-7, and it outperforms the other state-of-the-art CD methods, as shown in Table 1. In addition, because the three decoding strategies have limited impact on the performance of STDecoder-CD, we can conclude that TransCNN contributes most to the success of our algorithm. Second, the CD performance of STDecoder-CD is not completely free from the influence of the decoding strategies. For example, a slight downward trend in accuracy metrics and visual quality is visible from Type I to Types II and III. A slight upward trend in the computational efficiency from Type I to Types II and III can also be found in Table 2. Experimental results show that the Type I decoder can obtain finer change details than Types II and III, possibly due to its multi-scale design and the wide use of deep supervision. Third, the feature extraction ability of TransCNN is so powerful that it might conceal the contribution from the decoders. If so, the importance of the decoder in Siamese CD networks may be underestimated. For example, BIT and the Type III model use the same decoder (i.e., cross-attention, the core part of the transformer decoder) to enhance the original features with the context-rich tokens by modeling their relations. In Table 3, the CD performance of Type III STDecoder-CD is only suboptimal, whereas [PERSON] et al. [19] verified that the achieved performance of BIT with the cross-attention decoder is even better than BIT without it. This implies that, in some other cases, a well-designed decoder can play a more important role in identifying changes in objects. Fourth, it is demonstrated that our model has good generalization ability and transferability from PIESAT-CD to CD_Data_GZ, but it does not generalize well from CDD to CD_Data_GZ. CD_Data_GZ differs from CDD in terms of the sensor type and definitions of change. Therefore, in a follow-up study, it is necessary to create a general pretrained model for STDecoder-CD using sufficient training samples covering different changing objects, imaging conditions, and sensor types. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Decoder** & **Parameters** & **Precision** & **Recall** & **F1 Score** & **IOU** \\ \hline Original Type I & **40.84M** & **0.8851** & 0.8316 & 0.8575 & 0.7506 \\ Type I using & 51.79M & 0.8829 & **0.8357** & **0.8587** & **0.7523** \\ Difference Detector II & & **52.45M** & **0.8847** & **0.8294** & **0.8562** & **0.7485** \\ Type II using & & & & & \\ “many-to-many” & 54.95M & 0.8834 & 0.8176 & 0.8492 & 0.7380 \\ strategy & & & & & \\ \hline Original Type III & 53.89M & 0.8710 & **0.8350** & 0.8526 & 0.7431 \\ Type III without & & **53.16M** & **0.8792** & 0.8302 & **0.8540** & **0.7452** \\ transformer decoder & & & & & \\ \hline \hline \end{tabular} Best results are highlighted in bold.  
\end{table} 
Table 3: The ablation study using the PIESAT-CD dataset.