A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All-Sky Imagers

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dc.contributor.authorOkoh, Daniel
dc.contributor.authorCesaroni, Claudio
dc.contributor.authorRabiu, Babatunde
dc.contributor.authorShiokawa, Kazuo
dc.contributor.authorOtsuka, Yuichi
dc.contributor.authorOgunjo, Samuel
dc.contributor.authorAkerele, Aderonke
dc.contributor.authorHabarulema, John Bosco
dc.contributor.authorNava, Bruno
dc.contributor.authorMigoya-Orué, Yenca
dc.contributor.authorJamjareegulgarn, Punyawi
dc.contributor.authorSeun, Adeniran
dc.contributor.authorAdama, Ogechi
dc.contributor.authorOchieng, George
dc.contributor.authorAmeh, James
dc.contributor.authorAwuor, Adero
dc.contributor.authorBaki, Paul
dc.date.accessioned2026-08-06T10:51:22Z
dc.date.available2026-08-06T10:51:22Z
dc.date.issued2025-06-01
dc.description.abstractEquatorial plasma bubbles (EPBs) disrupt satellite-based communication and navigation systems, particularly in equatorial regions. Reliable detection and classification of EPBs from all-sky imager (ASI) images are essential for accurate space weather monitoring and forecasting. This study presents a novel bootstrapping convolutional neural network (CNN) approach to optimize automated EPB detection on ASI images for operational space weather monitoring applications, and overcoming challenges related to image variability and imbalanced data sets. Data used for CNN training were obtained from the optical mesosphere thermosphere imagers ASI installed at the Space Environment Research Laboratory, National Space Research and Development Agency, Abuja during the period from 2015 to 2020. Our method involved training three sub-models, and aggregating their predictions. The CNN trainings were conducted on three sub-datasets of 3,000 images each, categorized as “EPB,” “Noisy/Cloudy” or “No EPB.” Three corresponding sub-models were developed from the CNN trainings. The three sub-model classifications independently gave prediction accuracies of 98.67%, 98.33%, and 95.83% on a reserved test data set of 600 images. Ensemble models further improved the model prediction accuracies to 99.17% and 99.33% for methods based on the mean of sub-model probabilities and the mode of sub-model classifications respectively. Our results indicate that the bootstrapping CNN technique enhanced the EPB detection accuracy, providing a powerful tool for real-time space weather monitoring applications, and implications for improving operational reliability of satellite-based navigation and communication in the equatorial region.
dc.identifier.citationEarth and Space Science, 12(6), 2025
dc.identifier.doi10.1029/2024EA004117
dc.identifier.issn23335084
dc.identifier.other2-s2.0-105008319504
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17034
dc.sourceEarth and Space Science
dc.subjectairglow images
dc.subjectall-sky imager
dc.subjectbootstrapping
dc.subjectconvolutional neural networks
dc.subjectequatorial plasma bubbles
dc.subjectimage processing
dc.subjectmachine learning
dc.titleA Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All-Sky Imagers
dc.typeArticle

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