Microwave Radiometer RFI Detection Using Deep Learning
Microwave Radiometer RFI Detection Using Deep Learning
复制标题
使用深度学习进行微波辐射计 RFI 检测
DOI：
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复制
发表时间：
2021
影响因子：
5.5
通讯作者：
J. Piepmeier
中科院分区：
文献类型：
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作者：
P. Mohammed;J. Piepmeier
射频干扰（RFI）是微波辐射计的一个风险，因为它们需要非常高的灵敏度。土壤湿度主动被动（SMAP）使命采用了一种积极的方法，利用专门的航天硬件和地面处理软件进行RFI探测和滤波。随着越来越多的传感器在不受保护或共享频谱中以更大的带宽进行观测，RFI检测仍然至关重要。本文提出了一种使用SMAP谱图数据作为输入图像的RFI检测深度学习方法。该研究利用迁移学习的好处来评估这种方法在微波辐射计中用于RFI检测的可行性。研究了著名的预训练卷积神经网络AlexNet、GoogleNet和ResNet-101。ResNet-101在验证数据方面提供了最高的准确性（99%），而AlexNet与SMAP检测的一致性最高（92%）。
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%).