# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 1 Introduction
A few papers have described the segmentation methods of interference through time-series detection [19], image processing [20], and polarization difference [21] but are either poor in robustness or require a large amount of non-interference comparison data, restricting their application. In contrast to the above methods, the proposed methods possess the advantages of low implementation complexity and high robustness and are free from predefining thresholds. Furthermore, the application of artificial intelligence has been proved by some very successful works recently, paving the way for further research on RFI detection. [PERSON], [PERSON] et al. [4] proposed a single-shot multi-box detector (SSD) method for multi-class RFI detection using rectangular bounding boxes to mark the RFI components in the time-frequency image. Similarly, [22] mentioned a detection method based on Faster R-CNN in the range-doppler domain. By contrast, SISNet achieves RFI segmenting, that is, pixel-level detection, which is more precise. Further, these methods require Fourier transformation of the echo before detection, which increases the data processing dimension and introduces additional calcu  
Figure 1: RFI-contaminated Sentinel-1 data examples. lations. In [23; 24], DCNN structures such as VGG and ResNet networks are used for RFI detection. The authors used a binary classification method to distinguish between images with and without interference. According to the comparison test results below, compared to these methods, the IQDN network can detect the presence of interference faster while maintaining similar accuracy. The research is carried out in the following manners. In the beginning, the interference-targeted quick-detection network (IQDN) is constructed on the basis of the modified MobileNet V2 network, which replaces inverted residual blocks with ghost blocks and adds attention mechanisms at the head of the network to spot the existence of RFI in the quick-look images of the MID dataset according to the difference between an image with RFI and an image without RFI. Then, an improved U2 Net called 'SISNet' is proposed and employed to segment the RFI artifacts in the image domain on the quick-look images of the PAIS dataset. The following states the main contributions of this paper:  
(1) The IQDN algorithm designed upon the modified MobileNet V2 [25] network is proposed, which can effectively detect the existence of RFI in the image domain. It surpasses the cutting-edge algorithms using current convolutional neural networks (CNNs) such as VGG-16, ResNet, and MobileNet in inference time consumption while maintaining a certain accuracy;  
(2) An RFI segmentation algorithm designed upon SISNet is built. In contrast to current semantic segmentation networks, the SISNet advances the RFI segmentation performance significantly according to hypothesis testing. Moreover, the SISNet can extract features of the RFI without interference-free comparison images, reducing repetitive work for data collection. The rest of this paper unfolds as follows. Section 2 elaborates the mechanism of RFI formation and its classification. Section 3 explains the construction of the MID and PAIS datasets and introduces DCNN and semantic segmentation networks. Section 4 elaborates the IQDN algorithm based on the modified MobileNet V2 network and the interference segmentation algorithm named SISNet. Section 5 exhibits the results acquired from the experiments on the IQDN and SISNet; this is followed by the discussion in Section 6 and conclusions in Section 7.