Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection. (arXiv:2201.03016v2 [eess.IV] UPDATED)

Web: http://arxiv.org/abs/2201.03016

The detection of early signs of volcanic unrest preceding an eruption, in the
form of ground deformation in Interferometric Synthetic Aperture Radar (InSAR)
data is critical for assessing volcanic hazard. In this work we treat this as a
binary classification problem of InSAR images, and propose a novel deep
learning methodology that exploits a rich source of synthetically generated
interferograms to train quality classifiers that perform equally well in real
interferograms. The imbalanced nature of the problem, with orders of …

arxiv detection learning transformers vision