# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 1 Introduction  
Synthetic aperture radar (SAR), an active microwave remote sensing tool, provides important statistics for observing and comprehending changes taking place on Earth [1]. The high-resolution, all-weather image acquisition capabilities [2; 3; 4] allow SAR to be applied in a variety of fields, such as earth science, earth system monitoring [1; 3; 4; 5], etc. In recent years, SAR satellites have developed rapidly. There are more than 100 SAR satellite constellations worldwide, and the market value exceeds 10 billion. On the one hand, space-borne SAR is currently focusing on advancing the resolution and swath to a new level. On the other hand, the robustness of SAR needs to be further improved. Since SAR is a broadband active remote sensing radar system, radio frequency interference (RFI) is one of the biggest challenges and has various forms and sources (see Figure 1); in addition, some non-human factors often affect SAR observations, such as atmospheric [2; 3; 4; 5; 6] or boundary noise [7]. At present, with the use of electronic equipment increasing rapidly, the electromagnetic environment is becoming more and more complex. For example, TV broadcast signals, wireless communications, active jamming signals, and other non-cooperative SAR signals interfere with SAR observations. These incoherent electromagnetic signals appear as multiple bright lines or curved stripes in the SAR image domain [8] or appear in the form of dense raindrops as shown by the different colored artifacts in Figure 1, which seriously undermine the performance of SAR observation of the land and sea. In the spirit of ensuring overall data quality, it is imperative to detect and mark the RFI artifacts of SAR images instead of disregarding the RFI-contaminated data [9]. It is well known that any frequency band allocated for remote sensing of the Earth will be affected by RFI. However, due to the higher density of spectrum usage, lower frequency bands may be affected more by RFI. In recent years, L-band radars have reported more RFI interference [10]. In addition, C-band radars [11], such as Sentinel-1 and Gaofen-3 satellites; X-band radars [12], such as Terra-SAR satellites; and K-band radars [13] have all found some signs of RFI, with other, weaker RFIs remaining undetected. Urged by the demand of an intelligent monitoring system that will automatically mark images with RFI artifacts, this article concentrates on RFI artifact detection and segmentation in C-band Sentinel-1 SAR data. Specifically, this paper aims to solve the problem of serious ill-distribution of positive and negative samples caused by a small number of RFI samples in massive SAR data, as well as the multi-scale problem caused by the multi-modal characteristic of SAR interference, including the size and performance of RFI artifacts. With different forms, an intelligent RFI artifact quick detection and marking method is proposed. Our goal is to quickly screen RFI samples in the images and accurately mark the exact parts of SAR images affected by RFI. In early practices of RFI detection, researchers mainly used high-order statistical information of the signal to detect RFI. Among them, [PERSON] proposed the method of using spectral kurtosis [14] to characterize non-stationary RFI signals. Later, [PERSON] et al. used simulated pulse sinusoidal signals to conduct a sensitivity test on the spectrum kurtosis algorithm [15]. In addition, [PERSON] and [PERSON] combined the kurtosis and skewness statistics and proposed the JB detection statistic [16], which was later used for outlier detection. Different from previous studies of RFI based on raw data [1; 3; 10; 17; 18], our research is conducted on quick-look images of Sentinel-1 single-look complex (SLC) or ground range detected (GRD) data. Work related to interference segmentation on quick-look images has been rarely investigated previously.