  Satellite images have the potential to detect volcanic deformation prior to
eruptions, but while a vast number of images are routinely acquired, only a
small percentage contain volcanic deformation events. Manual inspection could
miss these anomalies, and an automatic system modelled with supervised learning
requires suitably labelled datasets. To tackle these issues, this paper
explores the use of unsupervised deep learning on satellite data for the
purpose of identifying volcanic deformation as anomalies. Our detector is based
on Patch Distribution Modeling (PaDiM), and the detection performance is
enhanced with a weighted distance, assigning greater importance to features
from deeper layers. Additionally, we propose a preprocessing approach to handle
noisy and incomplete data points. The final framework was tested with five
volcanoes, which have different deformation characteristics and its performance
was compared against the supervised learning method for volcanic deformation
detection.
