Radio Frequency Interference in Synthetic Aperture Radar Images

This article presents a methodology for localizing radio frequency interference (RFI) signals in Synthetic Aperture Radar (SAR) images acquired from Sentinel-1 SAR satellites. RFI are caused by on-ground radars, and their detection and localization thus provide valuable information for decision makers. In this study, an unsupervised deep learning model based on a Convolutional Autoencoder is used to detect and localize RFI signals in SAR images. The CAE reconstructs the SAR images, excluding RFI signals and other large-scale anomalies. Anomalies are detected by comparing the original images with their reconstructions, and a secondary classification scheme is used to identify RFI signals among the detected anomalies. Results show that the proposed method detects and localizes RFI signals, even in complex regions. The automatic localization of RFI signals in SAR images can enhance various applications such as maritime domain awareness and border surveillance.