Abstract
Monitoring volcanic deformations allows us to track dynamic states of a volcano and to know where an eruptions could happen. Spaceborne Synthetic Aperture Radar (SAR) and SAR interferometry (InSAR) techniques created an opportunity to track volcanoes globally, even in inaccessible regions without ground measuring stations.This paper proposes a convolutional neural network (CNN) for detection of volcanic deformations in InSAR velocity maps. We had only a small amount of velocity maps over the region of central South American Andes, therefore the synthetic data are used to train the model from scratch. In the region of interest, the velocity maps contain the patterns of salt lakes and slope induced signal which confuse CNN models trained on synthetic data.In order to bridge the gap between the synthetic and real data, the hybrid synthetic-real data set is used for fine-Tuning the model. The hybrid set consists of the real background signal data and synthetic volcanic data. Four fine-Tuning sets which were created by different combinations of the original hybrid data, the filtered hybrid data, and simulated data have been used and compared with each other. Besides, we compared four fine-Tuning approaches to determine where and how to fine-Tune the model. Results show significant improvement in performance by majority of the approaches, and training the last or last two layers have given the best results. In addition, using the FT1 (containing only hybrid set), and FT4 (containing all sets) improved the area under the curve receiver operating characteristic (AUC ROC) from 55% to 86% and 88% respectively.
Original language | English |
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Pages (from-to) | 85-92 |
Number of pages | 8 |
Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Volume | 5 |
Issue number | 3 |
DOIs | |
State | Published - 17 May 2022 |
Event | 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission III - Nice, France Duration: 6 Jun 2022 → 11 Jun 2022 |
Keywords
- Deep learning
- Fine-Tuning
- InSAR
- InceptionResNet v2
- Synthetic data
- Velocity maps
- Volcanic deformations