# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 5 Experimental Results
### Results of the Interference Segmentation Experiment
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **VGG** & **ResNet50** & **SENet** & **MobileNetV2** & **Ours** \\ \hline Parameters (M) & 138.35 & 23.51 & 26.02 & 2.22 & 1.68 \\ Flops (G) & 15.48 & 4.09 & 4.10 & 5.55 & 5.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of model size of different interference detection networks used in experiments. After cross-validation experiments, the results of mIoU, accuracy, precision, and F1 of each network are shown in Table 5 following. The standard deviation is calculated from each subset of the five-fold cross-validation method. In addition, the results of all indicators are selected based on the epoch with the best average score of mIoU. The attained results in Table 5 point out that FCN exhibits poor performance for the RFI artifact segmentation task. The comparative validation results of UNet++ and UNet show that deepening the network does not necessarily improve the segmentation performance. At the same time, the experimental results of UNet and U2 Net show that the lightweight U2 Net can achieve similar performance under the premise of using fewer parameters, demonstrating the superiority of the U2 Net network for the RFI artifact segmentation task. Finally, comparing the results of SISNet and U2 Net, we can reach the conclusion that the improvements proposed in this paper can further improve the performance of U2 Net. Figure 17: The average training loss curve and evaluation indicators of different networks with training epochs. Error bars were added in the last 10 epochs: (**a**) average training loss, (**b**) average training mIoU, (**c**) average training precision, and (**d**) average training F1. Figure 18: The SISNet training mIoU curve and loss curve with training epochs in the five-fold cross-validation experiments: (**a**) the mIoU results of five subtests and the average mIoU and (**b**) the loss of five subtests and the average loss. To better demonstrate the good effect of SISNet on interference segmentation, we select six typical scenarios that were not used in training. These scenarios contain various land covers, such as mountains, lakes, seaside, and islands, as well as different types of RFI artifacts. The data acquisition orbit numbers of these scenarios are shown in Table 6.  
During the experiment, we input the resized quick-look images of these scenarios into SISNet and other networks for comparative experiments. The output segmentation results obtained by each network are shown in Figure 20. \begin{table}
\begin{tabular}{l l} \hline \hline
**Scene Number** & **Data Acquisition Orbit Number** \\ \hline
1 & S1A\_IW\_GRDH\_1 SDV\_2010629T155008\_20210629T155033\_038557\_048 CEBF\_FE4A \\
2 & S1A\_IW\_GRDH\_1 SDV\_20210815T150830\_20210815T15085\_039242\_04A202\_A30A \\
3 & S1B\_IW\_GRDH\_1 SDV\_20210701T034259\_20210701T034324\_027595\_03483C\_0452 \\
4 & S1B\_IW\_GRDH\_1 SDV\_20210805T154051\_20210805T154116\_028113\_035A96\_4115 \\
5 & S1A\_IW\_SLC\_1 SDV\_20220124T031552\_20220124T031619\_041597\_04F2A1\_B042 \\
6 & S1B\_IW\_GRDH\_1 SDV\_20210607T094904\_20210607T094932\_027249\_03413A\_E4 CB \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The data acquisition orbit numbers of the scenarios used in the RFI segmentation experiment. Figure 19: Comparison of average mIoU results of different semantic segmentation networks. Validation method: 5-fold cross-validation. (**a**) SISNet vs. FCN8s, (**b**) SISNet vs. UNet, (**c**) SISNet vs. UNet++, and (**d**) SISNet vs. U2 Net.