Fine-Tuning cnns for decreased sensitivity to non-volcanic deformation velocity signals

T. Beker, H. Ansari, S. Montazeri, Q. Song, X. X. Zhu

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

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 languageEnglish
Pages (from-to)85-92
Number of pages8
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume5
Issue number3
DOIs
StatePublished - 17 May 2022
Event2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission III - Nice, France
Duration: 6 Jun 202211 Jun 2022

Keywords

  • Deep learning
  • Fine-Tuning
  • InSAR
  • InceptionResNet v2
  • Synthetic data
  • Velocity maps
  • Volcanic deformations

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