Frugal Satellite Image Change Detection with Deep-Net Inversion
Hichem Sahbi Sebastien Deschamps
Change detection in satellite imagery seeks to find occurrences of targeted
changes in a given scene taken at different instants. This task has several
applications ranging from land-cover mapping, to anthropogenic activity
monitory as well as climate change and natural hazard damage assessment.
However, change detection is highly challenging due to the acquisition
conditions and also to the subjectivity of changes. In this paper, we devise a
novel algorithm for change detection based on active learning. The proposed
method is based on a question and answer model that probes an oracle (user)
about the relevance of changes only on a small set of critical images (referred
to as virtual exemplars), and according to oracle's responses updates deep
neural network (DNN) classifiers. The main contribution resides in a novel
adversarial model that allows learning the most representative, diverse and
uncertain virtual exemplars (as inverted preimages of the trained DNNs) that
challenge (the most) the trained DNNs, and this leads to a better re-estimate
of these networks in the subsequent iterations of active learning. Experiments
show the out-performance of our proposed deep-net inversion against the related
work.