Anomaly detection for the identification of volcanic unrest in satellite imagery
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Abstract (arXiv)
Satellite images have the potential to detect volcanic deformation prior to eruptions, but while a vast number of images are routinely acquired, only a small percentage contain volcanic deformation events. Manual inspection could miss these anomalies, and an automatic system modelled with supervised learning requires suitably labelled datasets. To tackle these issues, this paper explores the use of unsupervised deep learning on satellite data for the purpose of identifying volcanic deformation as anomalies. Our detector is based on Patch Distribution Modeling (PaDiM), and the detection performance is enhanced with a weighted distance, assigning greater importance to features from deeper layers. Additionally, we propose a preprocessing approach to handle noisy and incomplete data points. The final framework was tested with five volcanoes, which have different deformation characteristics and its performance was compared against the supervised learning method for volcanic deformation detection.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 32 references that are in the map. First posted on arXiv 28 May 2024.
Builds on
11 of 32 references.
References · 11
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
- 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
- 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
- 2019Deep Learning for Anomaly Detection: A Surveycited by 358
- 2022Diffusion Models for Medical Anomaly Detectioncited by 232
- 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
- 2021Pixel-wise Anomaly Detection in Complex Driving Scenescited by 109
- 2021Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Imagescited by 45
- 2019A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasetscited by 13
- 2022Self-supervised Contrastive Learning for Volcanic Unrest Detectioncited by 4
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Citing papers per year: 2025: 1.
1 paper.