# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 3 Experiment and Results
### Result
#### 3.2.3 Accuracy Comparison of Results  
The classification results in the Vaihingen data set test were collated and statistically compared with the accuracy evaluation results of the three models, as shown in Table 3. In the comparison, TransUnet had the highest precision in all categories except for the tree category, where SETRnet had the highest precision. In the comparison of recall, SETRnet and SwinUnet were the highest in the low vegetation and impervious surfaces categories, respectively. TransUnet had the highest recall in the rest of the categories. In the F1 score comparison, TransUnet had the highest F1 value in all the categories. In the Kappa comparison, SETRnet was 73.80%, SwinUnet was 77.50%, and TransUnet was the highest, at 80.54%. TransUnet in MIoU was the highest, at 56.25%, an improvement of 8.57% and 4.77% relative to SETRnet and SwinUnet, respectively. In the OA comparison, TransUnet remained the highest, at 85.55%, with SETRnet and SwinUnet at 80.50% and 83.29%, respectively.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **SETRnet** & **SwinUnet** & **TransUnet** \\ \hline Time(s) & 4401.1 & 2929.7 & 6119.4 \\ \hline Average time(s) & 110.03 & 73.24 & 152.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training time of the Vaihingen data set.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Category** & **Precision** & **Recall** & **F1 Score** & **Kappa** & **MIoU** & **OA** \\ \hline \multirow{5}{*}{SETRnet} & Impervious surfaces & 79.30\% & 85.46\% & 82.26\% & & & \\  & Building & 87.94\% & 83.53\% & 85.68\% & & & \\  & Low vegetation & 61.40\% & **78.88\%** & 69.05\% & & & \\  & Tree & **89.74\%** & 76.53\% & 82.61\% & & & \\  & Car & 70.35\% & 19.67\% & 30.75\% & & & \\  & Clutter/background & 0.00\% & 0.00\% & 0.00\% & & & \\ \hline \multirow{5}{*}{SwinUnet} & Impervious surfaces & 83.60\% & **85.58\%** & 84.58\% & & & \\  & Building & 88.53\% & 87.87\% & 88.20\% & & & \\  & Low vegetation & 67.52\% & 74.01\% & 70.62\% & & & \\  & Tree & 87.94\% & 84.68\% & 86.28\% & 77.50\% & 51.48\% & 83.29\% \\  & Car & 68.66\% & 29.83\% & 41.59\% & & & \\  & Clutter/background & 0.00\% & 0.00\% & 0.00\% & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training time of the Potsdam data set.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **SETRnet** & **SwinUnet** & **TransUnet** \\ \hline Time(s) & 4401.1 & 2929.7 & 6119.4 \\ \hline Average time(s) & 110.03 & 73.24 & 152.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training time of the Vaihingen data set.  
The classification results in the Potsdam data set test were collated and statistically compared with the accuracy evaluation results of the three models, as shown in Table 4. In the comparison of precision, SETRnet had the highest accuracy in the impervious surfaces category, SwinUnet had the highest accuracy in both car and clutter/background categories, and TransUnet had the highest accuracy in the building, low vegetation and tree item categories. In the recall comparison, SETRnet's accuracy was highest in the building and clutter/background categories, SwinUnet's accuracy was highest in the low vegetation and tree categories, and TransUnet's accuracy was highest in the impervious surfaces and car categories. In the F1 score comparison, SETRnet obtained the highest accuracy in the building category, SwinUnet in the impervious surfaces, low vegetation, and tree categories, and TransUnet in the car category. In the Kappa comparison, SwinUnet's accuracy was the highest, at 76.47-4.4% and 8.29% higher than SETRnet and TransUnet, respectively. SwinUnet's accuracy was still the highest in MIoU, at 63.62%, while SETRnet's and TransUnet's accuracies were 59.97% and 58%, respectively. In the comparison of OA, SwinUnet remained the highest, at 85.01%, with an improvement of 6.5% and 9.25% relative to the OA values of SETRnet and TransUnet, respectively.