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A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets
- Source :
- Anantrasirichai, P, Biggs, J, Albino, F & Bull, D 2019, ' A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets ', Remote Sensing of Environment, vol. 230, 111179 . https://doi.org/10.1016/j.rse.2019.04.032
- Publication Year :
- 2019
-
Abstract
- Satellites enable widespread, regional or global surveillance of volcanoes and can provide the first indication of volcanic unrest or eruption. Here we consider Interferometric Synthetic Aperture Radar (InSAR), which can be employed to detect surface deformation with a strong statistical link to eruption. Recent developments in technology as well as improved computational power have resulted in unprecedented quantities of monitoring data, which can no longer be inspected manually. The ability of machine learning to automatically identify signals of interest in these large InSAR datasets has already been demonstrated, but data-driven techniques, such as convolutional neutral networks (CNN) require balanced training datasets of positive and negative signals to effectively differentiate between real deformation and noise. As only a small proportion of volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine learning for detecting volcanic unrest is more challenging than many other applications. In this paper, we address this problem using synthetic interferograms to train the AlexNet CNN. The synthetic interferograms are composed of 3 parts: 1) deformation patterns based on a Monte Carlo selection of parameters for analytic forward models, 2) stratified atmospheric effects derived from weather models and 3) turbulent atmospheric effects based on statistical simulations of correlated noise. The AlexNet architecture trained with synthetic data outperforms that trained using real interferograms alone, based on classification accuracy and positive predictive value (PPV). However, the models used to generate the synthetic signals are a simplification of the natural processes, so we retrain the CNN with a combined dataset consisting of synthetic models and selected real examples, achieving a final PPV of 82%. Although applying atmospheric corrections to the entire dataset is computationally expensive, it is relatively simple to apply them to the small subset of positive results. This further improves the detection performance without a significant increase in computational burden (PPV of 100%). Thus, we demonstrate that training with synthetic examples can improve the ability of CNNs to detect volcano deformation in satellite images, and propose an efficient workflow for the development of automated systems.
- Subjects :
- FOS: Computer and information sciences
010504 meteorology & atmospheric sciences
Computer science
Computer Vision and Pattern Recognition (cs.CV)
0208 environmental biotechnology
Computer Science - Computer Vision and Pattern Recognition
detection
Soil Science
02 engineering and technology
01 natural sciences
Synthetic data
Interferometric synthetic aperture radar
FOS: Electrical engineering, electronic engineering, information engineering
Interferometric Synthetic Aperture Radar
Computers in Earth Sciences
0105 earth and related environmental sciences
Remote sensing
Neutral network
business.industry
Deep learning
Image and Video Processing (eess.IV)
Geology
Pattern recognition
Electrical Engineering and Systems Science - Image and Video Processing
Numerical weather prediction
Atmospheric noise
020801 environmental engineering
volcano
machine learning
13. Climate action
Satellite
Noise (video)
Artificial intelligence
business
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Anantrasirichai, P, Biggs, J, Albino, F & Bull, D 2019, ' A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets ', Remote Sensing of Environment, vol. 230, 111179 . https://doi.org/10.1016/j.rse.2019.04.032
- Accession number :
- edsair.doi.dedup.....65f2017edcf6248275a21e1e20e0ce02
- Full Text :
- https://doi.org/10.1016/j.rse.2019.04.032