  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 magnitude
fewer positive samples, coupled with the lack of a curated database with
labeled InSAR data, sets a challenging task for conventional deep learning
architectures. We propose a new framework for domain adaptation, in which we
learn class prototypes from synthetic data with vision transformers. We report
detection accuracy that amounts to the highest reported accuracy on a large
test set for volcanic unrest detection. Moreover, we built upon this knowledge
by learning a new, non-linear, projection between the learnt representations
and prototype space, using pseudo labels produced by our model from an
unlabeled real InSAR dataset. This leads to the new state of the art with 97.1%
accuracy on our test set. We demonstrate the robustness of our approach by
training a simple ResNet-18 Convolutional Neural Network on the unlabeled real
InSAR dataset with pseudo-labels generated from our top transformer-prototype
model. Our methodology provides a significant improvement in performance
without the need of manually labeling any sample, opening the road for further
exploitation of synthetic InSAR data in various remote sensing applications.

Image and Video Processing;Computer Vision and Pattern Recognition