# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 5 Experimental Results
### Results of the Interference Segmentation Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Mask RCNN** & **FCN8s** & **UNet** & **UNet++** & **U2 Net 1** & **Ours** \\ \hline mIoU (\%) & 77.05 \(\pm\) 0.38 & 72.69 \(\pm\) 0.12 & 85.68 \(\pm\) 0.12 & 79.33\(\pm\)0.40 & 85.38 \(\pm\) 0.21 & 87.46 \(\pm\) 0.26 \\ Accuracy (\%) & / & 95.16 \(\pm\) 0.20 & 97.63 \(\pm\) 0.06 & 96.56 \(\pm\) 0.03 & 97.61 \(\pm\) 0.05 & 98.01 \(\pm\) 0.06 \\ Precision (\%) & 77.55 \(\pm\) 0.18 & 74.16 \(\pm\) 1.93 & 87.70 \(\pm\) 1.04 & 83.72 \(\pm\) 0.18 & 87.76 \(\pm\) 0.28 & 89.15 \(\pm\) 0.48 \\ F1 (\%) & 75.46 \(\pm\) 1.52 & 66.68 \(\pm\) 0.74 & 84.97 \(\pm\) 0.05 & 76.89 \(\pm\) 0.62 & 84.74 \(\pm\) 0.22 & 87.09 \(\pm\) 0.76 \\ \hline \hline \end{tabular}  
* Here refers to the U2 NetP network.  
\end{table} 
Table 5: Comparison of 5-fold cross-validation results with \(\pm\) one standard deviation value of different semantic segmentation networks on PAIS dataset. Through the comparison of the test results, we find that the segmentation mask output by SISNet is closest to the label image, and no matter how the size of the area occupied by the RFI artifact changes in the test image, the prediction mask output by the model has better performance in terms of mask integrity and numerical accuracy of RFI instances. To illustrate the significant improvement the proposed model possesses over the comparison models, we conduct statistical analysis based on hypothesis testing on the tested indicator results. Because the samples in the test set are independent random samples, the sampling distribution conforms to the normal distribution as the sample size reaches 100, and indicators such as IoU are continuous variables, while the comparison between models is a categorical variable; it is suitable for hypothesis testing for two-sample cases. We conduct test experiments on 20 data samples randomly selected from the test set of the PAIS dataset and obtain the sample mean, sample standard deviation, etc. of the IoU and other indicators of the test set samples under different models. Take the IoU of SISNet  
Figure 20: Comparison of interference segmentation experiment results: (**a1–a6**) original quick-look images (the numbering sequence is consistent with Table 5 above); (**b1–b6**) label image; (**c1–c6**) FCN8s outputs; (**d1–d6**) UNet++ outputs; (**e1–e6**) U2 Net outputs; (**f1–f6**) our proposed SISNet outputs. and U2 Net as an example. With small samples, the \(t\) distribution is used to establish the critical region. The formulas of the \(t\) distribution are expressed as follows:  
\[t=\frac{\mu_{1}-\mu_{2}}{\sigma_{\mu_{1},\mu_{2}}}, \tag{15}\]  
\[\sigma_{\mu_{1},\mu_{2}}=\sqrt{\frac{N_{1}\sigma_{1}{}^{2}+N_{2}\sigma_{2}{}^{ 2}}{N_{1}+N_{2}-2}}\sqrt{\frac{N_{1}+N_{2}}{N_{1}N_{2}}}, \tag{16}\]  
where \(N_{1}=N_{2}=20\), \(\mu_{1}\) stands for the sample mean of SISNet's test IoU results, and the standard deviation is \(\sigma_{1}\). The sample mean of U2 Net's test IoU results is \(\mu_{2}\), and the standard deviation is \(\sigma_{2}\). We set the confidence level at 95%, and the corresponding critical region is \(t(critical)=\pm 2.002\) in a two-tailed test. The null hypothesis \(H_{0}\) can be established as \(\mu_{1}=\mu_{2}\), and the alternative hypothesis \(H_{1}\) is \(\mu_{1}\
eq\mu_{2}\). If the obtained \(t\) score falls beyond the critical region, the null hypothesis is rejected, which means that \(H_{1}\) is accepted and the mean of the IoU of the two groups is significantly different. The specific values of the sample mean and sample standard deviation of the IoU results of the above two tests are shown in Table 7 below. The \(t\) score obtained at this time is \(t(obtained)=4.361\). Based on the statistics, it can be concluded that \(t(obtained)\) falls out of the critical region, which means that the hypothesis \(H_{0}\) is rejected. As a result, there exists a significant difference between the IoU sample means of the two groups of test results.