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%).

中文翻译：

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使用深度学习进行微波辐射计 RFI 检测


由于微波辐射计需要非常高的灵敏度，射频干扰 (RFI) 是一种风险。土壤湿度主动被动 (SMAP) 任务采用了一种积极的方法，使用专用航天硬件和地面处理软件来检测和过滤 RFI。随着越来越多的传感器推动在未受保护或共享频谱中以更大的带宽进行观察，RFI 检测仍然至关重要。本文介绍了一种使用 SMAP 频谱图数据作为输入图像进行 RFI 检测的深度学习方法。该研究利用迁移学习的优势来评估该方法在微波辐射计中 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 提供了验证数据的最高准确度 (99%)，而 AlexNet 与 SMAP 检测表现出最高的一致性 (92%)。