# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 5 Conclusions  
In this study, we investigated which transformer model is more suitable for remote-sensing image feature segmentation by evaluating the performance of different transformer models. In this study, first, three transformer models were briefly described, and the  
Figure 14: Classification results of CNN and transformers on the Vaihingen data set. The red boxes are where the differences between the five network segmentations are large.  
network structure of the transformer model was separately constructed. In this study, experiments were conducted on two data sets, namely the Vaihingen and Potsdam data sets, and the SETRnet, SwinUnet, and TransUnet models were compared by conducting a visual analysis of feature-segmentation results and by assessing their accuracy and training time. The three models were further discussed and analyzed with CNNs. This research will aid in the understanding of different transformer models and the selection of more-suitable transformer models for remote-sensing image feature segmentation in future experiments. The results indicated that SwinUnet performed better on the large-scale Potsdam data set thanks to its excellent global semantic interaction and pixel-level segmentation prediction ability. TransUnet benefits from its network structure jointly constructed by a transformer and a CNN, and it has the highest accuracy on the small-scale Vaihingen data set. Compared with SwinUnet and TransUnet, SETRnet is not suitable for the segmentation extraction of remote-sensing image features. At the same time, the experimental results also show that a transformer has obvious advantages for the segmentation of large-scale objects, but the pure transformer structure is not suitable for remote-sensing image segmentation. For different scales of remote-sensing data, researchers need to choose appropriate transformer models and improve methods.  
In the future, we should pay more attention to the following two areas. First, the transformer model's ability to extract the edges of features is insufficient. We should address the issue of the transformer model's overly focusing on the semantic relationship between using the global details and ignoring the edge details. Second, we should invest in expanding the application of different transformer models in the segmentation and extraction of remote-sensing image features and further verify their effectiveness.  
Conceptualization, M.Y.; methodology, F.Q. and M.Y; formal analysis, M.Y; investigation, M.Y.; data curation, M.Y.; writing--original draft preparation, M.Y.; writing--review and editing, F.Q.; visualization, M.Y.; supervision, F.Q. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the High-Resolution Satellite Project of the State Administration of Science, Technology, and Industry for the National Defense of PRC, grant number 80-Y50G19-9001-22/23; the National Science and Technology Platform Construction, grant number 2005 DKA32300; the Key Projects of the National Regional Innovation Joint Fund, grant number U21A2014; the Ministry of Education, grant number 16 JJD770019; and the Open Program of Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Henan Province, grant number G202006.  
Not applicable.  
The authors declare no conflict of interest.