Ionospheric Echo Detection Using Semantic Segmentation

We aim to replicate the experiments described in the paper "Ionospheric Echo Detection in Digital Ionograms Using Convolutional Neural Networks" from the Advancing Earth and Space Science Journal submissions 2021.

Abstract

An ionogram is a graph of the time that a vertically transmitted wave takes to return to the earth as a function of frequency. Time is typically represented as virtual height, which is the time divided by the speed of light. The ionogram is shaped by making a trace of this height against the frequency of the transmitted wave. Along with the echoes of the ionosphere, ionograms usually contain a large amount of noise and interference of different nature that must be removed in order to extract useful information. In the present work, we propose a method based on convolutional neural networks to extract ionospheric echoes from digital ionograms. From the extracted traces, ionospheric parameters can be determined and electron density profile can be derived.

Neural Network Architecture

Results

Curated Data

• data
• masks

References

• Ionospheric Echo Detection in Digital Ionograms Using Convolutional Neural Networks
• Fully Convolutional Networks for Semantic Segmentation
• U-Net: Convolutional Networks for Biomedical Image Segmentation