Deep Learning for RFI Artifact Recognition in Sentinel-1 Data

Beyond the variety of unwanted disruptions that appear quite frequently in synthetic aperture radar (SAR) measurements, radio-frequency interference (RFI) is one of the most challenging issues due to its various forms and sources. Unfortunately, over the years, this problem has grown worse. RFI artifacts not only hinder processing of SAR data, but also play a significant role when it comes to the quality, reliability, and accuracy of the final outcomes. To address this issue, a robust, effective, and-importantly-easy-to-implement method for identifying RFI-affected images was developed. The main aim of the proposed solution is the support of the automatic permanent scatters in SAR (PSInSAR) processing workflow through the exclusion of contaminated SAR data that could lead to misinterpretation of the calculation results. The approach presented in this paper for the purpose of recognition of these specific artifacts is based on deep learning. Considering different levels of image damage, we used three variants of a LeNet-type convolutional neural network. The results show the high efficiency of our model used directly on sample data.

Automated summary

The study focuses on addressing radio-frequency interference (RFI) in SAR measurements through deep learning to identify RFI-affected images, thereby eliminating misinterpretation of calculation results caused by contaminated data. By using three variants of a LeNet-type convolutional neural network, the model demonstrated high efficiency on sample data.