Publikationsdatum:
2024-04-15
Beschreibung:
This paper addresses the classification of images depicting the eruptive activity of Mount
Etna, captured by a network of ground-based thermal cameras. The proposed approach utilizes
Convolutional Neural Networks (CNNs), focusing on pretrained models. Eight popular pretrained
neural networks underwent systematic evaluation, revealing their effectiveness in addressing the
classification problem. The experimental results demonstrated that, following a retraining phase
with a limited dataset, specific networks such as VGG-16 and AlexNet, achieved an impressive
total accuracy of approximately 90%. Notably, VGG-16 and AlexNet emerged as practical choices,
exhibiting individual class accuracies exceeding 90%. The case study emphasized the pivotal role
of transfer learning, as attempts to solve the classification problem without pretrained networks
resulted in unsatisfactory outcomes.
Beschreibung:
Supported by Italian Research Center on High Performance Computing Big Data and
Quantum Computing (ICSC), project funded by European Union—NextGenerationEU—and National
Recovery and Resilience Plan (NRRP)—Mission 4 Component 2 within the activities of Spoke 3
(Astrophysics and Cosmos Observations). Sonia Calvari also acknowledges the financial support of
the Project FIRST ForecastIng eRuptive activity at Stromboli volcano (Delibera n. 144/2020; Scientific
Responsibility: S.C.) Vulcani 2019.
Beschreibung:
Published
Beschreibung:
124-137
Beschreibung:
OSV3: Sviluppo di nuovi sistemi osservazionali e di analisi ad alta sensibilità
Beschreibung:
JCR Journal
Schlagwort(e):
Etna Volcano
;
Lava Fountains
;
classification of events
;
04.08. Volcanology
Repository-Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Materialart:
article
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