# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 5 Experimental Results
### Results of the Interference Segmentation Experiment
Given the direction of the difference, we can also note that the average IoU result of SISNet outperforms U2 Net's. Similarly, we can calculate the \(t\) score of each indicator of SISNet and compare it to other models one by one. The confidence level is set to 95%. By looking up to the \(t\) distribution table, we find that the critical region \(t(critical)=\pm 2.002\). The calculated \(t\) scores are shown in Table 8 following, wherein the numbers with superscripts represent the significance of the test indicators of SISNet relative to the comparison models. From the above results, since most \(t(obtained)\) values fall outside of the critical region, it is obvious that the proposed SISNet significantly outperforms the other models in most indicators representing segmentation performance. Specifically, SISNet surpasses U2 Net in IoU, accuracy and F1 score, while it is slightly inferior to U2 Net in precision. Moreover, SISNet performs better than FCN8s, UNet, and UNet++ in all indicators, but the accuracy does not improve significantly in contrast to UNet++. While achieving excellent segmentation performance, the proposed model has a smaller number of parameters and less computation than FCN, UNet, and UNet++, as shown in Table 9. Compared to the original U2 Net, the SISNet proposed achieved a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **SISNet** & **U2 Net 1** \\ \hline Sample mean (\(\mu\)) & \(\mu_{1}=91.05\) & \(\mu_{2}=88.15\) \\ Sample standard deviation (\(\sigma\)) & \(\sigma_{1}=2.39\) & \(\sigma_{2}=1.64\) \\ \hline \hline \end{tabular} 1 Here refers to the U2 NetP network.  
\end{table} 
Table 7: Sample means and sample standard deviation of SISNet’s and U2 Net’s test IoU results. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **FCN8s** & **UNet** & **UNet++** & **U2 Net 1** \\ \hline t(obtained) of IoU & 9.299 ** & 3.910 ** & 3.356 ** & 4.361 ** \\ t(obtained) of Accuracy & 3.222 ** & 3.631 ** & 0.084 & 6.236 ** \\ t(obtained) of Precision & 7.604 ** & 2.651 * & 3.223 ** & \(-1.178\) \\ t(obtained) of F1 & 8.670 ** & 5.477 ** & 3.084 ** & 4.277 ** \\ \hline \hline \end{tabular} 1 Here refers to the U2 NetP network. Note: \(n=20\), * \(p<0.05\), ** \(p<0.01\).  
\end{table} 
Table 8: The \(t\) scores of evaluation indicators of SISNet compared to other models. large improvement in image segmentation indicators such as mIoU and F1 score without significantly increasing the model parameters and computational load, which shows the superiority of SISNet. It is worth mentioning that owing to the limited computing ability, the lightweight version of U2 Net network is used in the experiment, but it still achieves better segmentation performance. If the improved technologies proposed in this article can be applied to the full version of U2 Net, better results should be obtained in theory. The ablation experiment exhibits the results of a neural network after removing essential modules to acknowledge the effect of these modules on the whole network. Therefore, we perform ablation experiments by discarding the RFB-d modules and attention modules. The experiments also conduct five-fold cross-validation in the training stage. Table 10 summarizes the average scores of mIoU, accuracy, precision, and F1 scores of diverse networks, which validates the usefulness of these modules.