| Title: | GS5-3 A penalized motion detection model for extracting ionospheric echoes from low signal-to-noise ratio Ionogram video images |
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| Publication: | ICAROB2024 |
| Volume: | 29 |
| Pages: | 1025-1031 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2024.GS5-3 |
| Author(s): | Yuu Hiroshige, Akiko Fujimoto, Akihiro Ikeda, Shuji Abe, Akimasa Yoshikawa |
| Publication Date: | February 22, 2024 |
| Keywords: | Ionospheric Observation, Computer Vision, Motion Detection, Time-series data |
| Abstract: | Measuring the altitude distribution of electron density in the upper atmosphere, known as the ionosphere, using HighFrequency radio wave reflections often causes the low signal-to-noise ratio of ionospheric echoes due to radio frequency interference. We propose a model for converting low-signal-to-noise-ratio ionospheric echo video images (Ionogram) into noise-reduced images using image processing techniques, for tracing the ionospheric echoes from Ionogram. The proposed method consists of three processing parts: noise removal optimized for individual Ionogram images, extraction of ionospheric echoes by penalized background subtraction technique, and fine-tuning of ionospheric echo signals using a minimum spanning tree algorithm. The proposed model successfully reproduces fine Ionograms with 98% recall and 99% precision. |
| PDF File: | https://alife-robotics.co.jp/members2024/icarob/data/html/data/GS/GS5-3.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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