  Satellite image change detection aims at finding occurrences of targeted
changes in a given scene taken at different instants. This task is highly
challenging due to the acquisition conditions and also to the subjectivity of
changes. In this paper, we investigate satellite image change detection using
active learning. Our method is interactive and relies on a question and answer
model which asks the oracle (user) questions about the most informative display
(dubbed as virtual exemplars), and according to the user's responses, updates
change detections. The main contribution of our method consists in a novel
adversarial model that allows frugally probing the oracle with only the most
representative, diverse and uncertain virtual exemplars. The latter are learned
to challenge the most the trained change decision criteria which ultimately
leads to a better re-estimate of these criteria in the following iterations of
active learning. Conducted experiments show the out-performance of our proposed
adversarial display model against other display strategies as well as the
related work.
