# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 5 Conclusions  
This study puts forward three different types of decoder design schemes to decode a Siamese TransCNN-based backbone, namely, the STDecoder-CD series model. Type I STDecoder-CD has a UNet-like decoder. Type II STDecoder-CD is defined by a combination of three modules: Difference Detector, FPN, and FCN, and the encoder and decoder communicate mainly through FPN. Type III refines/updates the existing change feature map by using a transformer decoder with MHCA. We hope that our STDecoder-CD models help in improving the understanding of architecture design in this space.  
Using the CDD, PIESAT-CD and CD_data_GZ datasets as benchmarks, a series of comparative experiments and ablation experiments were conducted, and our findings show that:  
1. Due to its powerful change feature extraction and representation capabilities, the TransCNN-dominated STDecoder-CD model is superior to other compared SOTA methods (STANet, ChangeFormer, BIT, SiUNet3+CD, etc.) in terms of object location, boundary production, and quantitative metrics evaluation.
2. Even though different decoding strategies do not seem to impact the accuracy metrics to a significant level, the proposed STDecoder-CD series architecture is not completely free from the influence of the decoding strategies. The Type I decoding module gives the best CD performance.
3. Using the ablation or replacement strategy to modify the three proposed decoder architectures has a limited impact on their CD performance.  
**Author Contributions:** Conceptualization, B.Z. and X.L.; methodology, B.Z. and X.L.; software, B.Z.; validation, X.L. and Y.L.; formal analysis, B.Z., H.W. and P.T.; investigation, B.Z. and X.L.; resources, P.T.; data curation, N.O.; writing--original draft preparation, B.Z.; writing--review and editing, P.T.; visualization, B.Z. and X.L.; supervision, P.T.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Soft Science Research Plan item of Zhejiang Province, China, grant number 2022C35070.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets used in this study are publicly available: CDD is available at [[http://aistudio.baidu.com/aistudio/projectdetail/882508](http://aistudio.baidu.com/aistudio/projectdetail/882508)]([http://aistudio.baidu.com/aistudio/projectdetail/882508](http://aistudio.baidu.com/aistudio/projectdetail/882508)) (accessed on 20 February 2022); PIESAT-CD is available at [[https://captain-whu.github.io/PRCV2021_RS/dataset.html](https://captain-whu.github.io/PRCV2021_RS/dataset.html)]([https://captain-whu.github.io/PRCV2021_RS/dataset.html](https://captain-whu.github.io/PRCV2021_RS/dataset.html)) (accessed on 20 February 2022); and CD_Data_GZ is available at [[https://pan.baidu.com/s/12](https://pan.baidu.com/s/12) Ln-ncD15 YNu1T28 pzC0 rA]([https://pan.baidu.com/s/12](https://pan.baidu.com/s/12) Ln-ncD15 YNu1T28 pzC0 rA) (accessed on 20 February 2022).  
**Acknowledgments:** Constructive criticism and helpful review comments on this manuscript were provided by several anonymous reviewers, and the authors would like to thank them here. We also thank [PERSON] in the PIESAT International Information Technology Limited for providing the PIESAT-CD dataset.  
**Conflicts of Interest:** The authors declare no conflict of interest.