# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 7 Conclusions  
In this article, we first build two new datasets, named the 'MID dataset' and the 'PAIS dataset', using Sentinel-1 SAR quick-look images for RFI detection and segmentation tasks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ablation Approaches** & **mIoU** & **Accuracy** & **Precision** & **F1** \\ \hline No improvements (\%) & 85.38 & 97.61 & 87.76 & 84.74 \\ No RFB-d (\%) & 87.03 & 97.90 & 88.01 & 86.55 \\ No attention modules (\%) & 86.54 & 97.81 & 87.99 & 86.02 \\ Proposed (\%) & 87.46 & 98.01 & 89.15 & 87.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of ablation experiments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **FCN8s** & **UNet** & **UNet++** & **U2 Net 1** & **Ours** \\ \hline Parameters (M) & 33.60 & 34.53 & 36.63 & 1.27 & 1.27 \\ Flops (G) & 104.81 & 262.10 & 554.65 & 71.47 & 76.56 \\ \hline \hline \end{tabular}  
* Here refers to the U2 NetP network.  
\end{table} 
Table 9: Comparison of model size of different semantic segmentation networks used in experiments. Then, we present IQDN on the basis of MobileNet, which provides a novel RFI detector to quickly and precisely detect RFI in the image domain of the MID dataset. The test experiment shows IQDN is less time-consuming than other CNNs. In addition, we propose an RFI segmentation network named SISNet based on U2 Net. It distinguishes RFI artifacts from terrain and ocean background by automatically outputting a pixel-level predicted mask, which can be used in RFI localization by combining the corresponding product information. Moreover, four different metrics, including mIoU, accuracy, precision, and F1 score, are adopted to assess the performance of SISNet. Statistical analysis based on the validation results and hypothesis testing indicate that SISNet outperforms state-of-the-art methods in RFI segmentation performance on the PAIS dataset. It not only saves researchers from searching for a large amount of interference-free contrast data but also realizes the preliminary localization of RFI through image segmentation. [PERSON] designed the experiment; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] wrote the paper; [PERSON], [PERSON] and [PERSON] supervised the program; [PERSON] revised the grammar errors of the paper; [PERSON] improved the English writing; [PERSON], [PERSON], [PERSON] and [PERSON] were responsible for data collection, classification, and labeling. All authors have agreed to publish the current version of the manuscript. This paper was funded partially by the Youth Project of High-Level Talent Recruiting Plan of Shaanxi Province (90728220019) and was co-financed by the Science and Technology Innovation Team of Shaanxi Province (2022 TD-38). The data that support the findings of this study are openly available on the official website of European Space Agency at [[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)), accessed on 9 September 2023). The authors appreciate all the anonymous reviewers and editors for their constructive comments and suggestions that greatly improved this paper. Meanwhile, the authors are thankful to the European Space Agency for providing free online downloading of the Sentinel-1 data. The authors declare no conflict of interest.