# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 3 Materials
### Dataset Creation
#### 3.1.1 Multi-Scale Interference Detection Dataset  
The impact of RFI on SAR determines that RFI's artifacts in the image after SAR imaging will present characteristics such as being multi-style and multi-scale. With the increasing demand for RFI detection and recognition, it is necessary to construct a multi-scale RFI dataset. For the RFI artifacts studied in this paper, there is no multi-scene, multi-style, and multi-scale RFI artifact image dataset available. Therefore, two new datasets for RFI detection and segmentation, named the 'MID dataset' and the 'PAIS dataset', come into being.  
The multi-scale interference detection (MID) dataset is set up using 130 quick-looks of SLC products obtained from the official website of the European Space Agency [29]. These RFI-contaminated SAR data are derived from observations of the Korean region by the Sentinel-1 satellites, and the time span ranges from May 2021 to May 2022. The MID dataset contains a variety of polarization modes and a total of about 30,000 slices with multiple resolutions, among which more than 5000 slices are contaminated by RFI.  
We carefully select 100 images with strong RFI features. Since 100 images are insufficient for deep learning, we use random cropping on these quick-look images and retain the new images that are large enough in size to expand the dataset. In addition, in view of the multi-scale characteristics of RFI, we crop these images into slices of 256 \(\times\) 256, 512 \(\times\) 512, and 1024 \(\times\) 1024 at a ratio of 4:1:1, and the total number of images is 30,000. Since some of the cropped images are still too large to be applied to CNN, these images are resized to 224 \(\times\) 224 and are manually divided into two categories: with RFI and RFI-free. This method can change the size of the area contaminated by RFI in the image while using the same quick-look image, which enhances the robustness of the network to multi-scale features. Figure 3 tells that the training data contain a variety of different types of RFI, and there is no need for a one-to-one match between RFI and non-RFI scenes.  
As is known, RFI only occupies a small area in the image. In the original two classification sample sets, the distributions of the two types of samples are not equal, as the number of images with RFI is relatively small. Therefore, we perform data augmentation only on the RFI-contaminated sample set in order to increase the number of these samples and balance the numbers of the two types of data. The data augmentation methods are applied at a 1:1:1:1 ratio (i.e., 5\(\times\) data augmentation) and include flipping, blurring, translating and affiliating. In the data of Sentinel-1, RFI artifacts are always distributed vertical to the along-track direction, so it is unnecessary to augment the data by rotating the pictures. After data augmentation, some problematic data are checked and manually removed. A set of samples after data augmentation is shown in Figure 4.  
In the final interference detection dataset, the number of samples with interference is 25,655, and the number of samples without interference is 22,270. Then, the dataset is randomized into training and testing sets at a ratio of 4:1. Descriptive information about the MID dataset is shown in Table 1 below: