Microwave Radiometer RFI Detection Using Deep Learning

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Priscilla N. Mohammed¹  Jeffrey R. Piepmeier¹ 

[1] NASA Goddard Space Flight Center, Greenbelt, MD, USA;

关键词: Deep learning;    microwave radiometry;    radio frequency interference (RFI);    transfer learning;   

DOI  :  10.1109/JSTARS.2021.3091873

来源: DOAJ

【 摘 要 】

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%).

【 授权许可】

Unknown