Radio Frequency Interference Detection in Passive Microwave Remote Sensing Using One-Class Support Vector Machines

Radio frequency interference (RFI) is a serious threat to the accurate estimation of critical geophysical parameters via passive microwave remote sensing and the presence of RFI in microwave radiometer measurements is increasing over time. On the other hand, the nature and the occurrence of RFI captured by radiometers are usually unknown making their detection and mitigation difficult. To overcome this challenge, this article presents a novel RFI detection algorithm that relies only on the information extracted from the RFI-free radiometer measurements which can be collected over oceans and rural areas with limited human activity, i.e., a one-class algorithm, to be implemented in future remote sensing radiometers. The algorithm transforms raw time-series radiometer measurements into a heterogeneous feature-based representation. Then, a feature selection algorithm identifies the most discriminant features to detect interference based on the probabilities of misdetections and false alarms. Finally, the optimal decision boundaries that discriminate the RFI-contaminated radiometer measurements from the RFI-free ones are computed via support vector machines (SVM) using only the RFI-free radiometer measurements. Regardless of the characteristics of RFI contamination, the algorithm, therefore, outputs a generalized decision boundary for RFI-free measurements. A performance evaluation of the proposed algorithm against the traditional RFI detection algorithms has been performed using simulated radiometer data, and the results have shown that the novel algorithm, unlike the traditional methods, can successfully detect RFI, even when the interference-to-noise ratio (INR) of the radiometer measurements is as low as $-18$ dB.