# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 3 Materials
### Dataset Creation
#### 3.1.2 Polygonal-Annotated Interference Segmentation Dataset  
The polygonal-annotated interference segmentation (PAIS) dataset adopts quick-looks of the GRD product corresponding to the SLC products of the MID dataset. The GRD images do not contain phase information and have reduced speckles due to multi-look processing. The dataset contains multiple polarization modes and has the characteristics of various types of RFI artifacts and scenes.  
Figure 5 presents some of the augmented data samples of the PAIS dataset shown from left to right in columns. These samples are adjusted from 130 quick-look images that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Observation Area** & **Time Span** & **Interference** & **Interference-Free** & **Image Size** \\  & & & **Sample Quantity** & **Sample Quantity** & \\ \hline MID dataset & Korea & 1 May 2021–30 May 2022 & 25,665 & 22,270 & 224 \(\times\) 224 \\ \hline \hline \end{tabular}
\end{table}
Table 1: General information of MID dataset.  
Figure 4: The data augmentations performed, including flipping, blurring, translating, and affiliating.  
Figure 3: Some samples of the MID dataset. (**left**) RFI-contaminated images. (**right**) RFI-free images.  
are resized to 572 \(\times\) 572 as the standard input size and then augmented to 2000 images via flipping, translating, noise-adding, and blurring operations. The data augmentation methods are applied at a 1:1:1:1 ratio. Furthermore, 100 more scenes of GRD products are selected from Sentinel-1's SAR observation data of other areas.  
The RFI-affected areas vary in number and size in each image. It takes days for us to use the Labelme 5.0.1 software to manually label the location of the interference in the image to form a semantic segmentation mask using the polygonal-annotated method and to save it as a JSON file. There are more than 4700 instances of RFI artifacts, covering a variety of styles and sizes. Then, we use the JSON files to generate the corresponding one-channel label images, modify the label images and the original images to have the same file names, match the image to the label, and store the files in two folders. Similarly, the data are randomly divided into a training set and validation set at a ratio of 9:1 during training. In summary, the PAIS dataset consists of six folders, including JPEGImages, Annotations, SegmentationClassPNG, TestImages, and TestSegmentationPNG. In these folders, JPEGImages contains 2000 images used for training and validating, while the Annotations and SegmentationClassPNG folders are the corresponding JSON annotation files and segmented label images, respectively. In the same way, the TestImages and TestSegmentationPNG folders consist of test images and their label images, respectively. Samples of the PAIS dataset are show in Figure 6:  
Information about the PAIS dataset is shown in Table 2 below:  
Figure 5: Some augmented data samples of PAIS dataset show from left to right in columns that the original image has been flipped, translated and blurred, and noise has been added, respectively.  
Figure 6: Samples of PAIS dataset. (**a**,**c**,**e**,**g**) are quick-look images contaminated by RFI. (**b**,**d**,**f**,**h**) are the annotated segmentation labels.  
The ground range coordinates of the GRD products are the slant range coordinates projected onto the ellipsoid of the earth, which means GRD data can be related to geographic information. In a sense, the location of RFI can be realized by segmenting the RFI of GRD data.