Abstract
Radio frequency interference (RFI) is a risk for microwave radiometers due to their requirement of very high sensitivity. The Soil Moisture Active Passive (SMAP) mission has an aggressive approach to RFI detection and filtering using dedicated spaceflight hardware and ground processing software. As more sensors push to observe at larger bandwidths in unprotected or shared spectrum, RFI detection continues to be essential. This article presents a deep learning approach to RFI detection using SMAP spectrogram data as input images. The study utilizes the benefits of transfer learning to evaluate the viability of this method for RFI detection in microwave radiometers. The well-known pretrained convolutional neural networks, AlexNet, GoogleNet, and ResNet-101 were investigated. ResNet-101 provided the highest accuracy with respect to validation data (99%), while AlexNet exhibited the highest agreement with SMAP detection (92%).
Highlights
RADIO frequency interference (RFI) detection in microwave brightness temperature data continues to be a problem of interest and several techniques have been developed to detect the presense of RFI in radiometer measurements, e.g. [1,2,3,4,5,6,7,8,9,10]
The RFI detection algorithms in the Soil Moisture Active Passive (SMAP) ground processing are drawn from this previous work and include energy detectors in both time and frequency referred to as the pulse and cross frequency detectors, the kurtosis method, which is a test for normality and polarimetric approaches, which search for anomalies in the 3rd and 4th Stokes parameters [4, 12, 13]
All networks provided very similar accuracy results for the validation data sets; AlexNet provided the best results when used for RFI detection on other test orbits
Summary
RADIO frequency interference (RFI) detection in microwave brightness temperature data continues to be a problem of interest and several techniques have been developed to detect the presense of RFI in radiometer measurements, e.g. [1,2,3,4,5,6,7,8,9,10]. The RFI detection algorithms in the SMAP ground processing are drawn from this previous work and include energy detectors in both time and frequency referred to as the pulse and cross frequency detectors, the kurtosis method, which is a test for normality and polarimetric approaches, which search for anomalies in the 3rd and 4th Stokes parameters [4, 12, 13]. These methods work best to filter RFI that is sparse in time and/or frequency. With the advent of graphics processing unit (GPU) technology for space use and the growing number of channels in digital receivers, it may be advantageous to run a deep learning algorithm on a GPU rather than conventional algorithms on a microprocessor
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