  In this paper, we devise a novel interactive satellite image change detection
algorithm based on active learning. The proposed framework is iterative and
relies on a question and answer model which asks the oracle (user) questions
about the most informative display (subset of critical images), and according
to the user's responses, updates change detections. The contribution of our
framework resides in a novel display model which selects the most
representative and diverse virtual exemplars that adversely challenge the
learned change detection functions, thereby leading to highly discriminating
functions in the subsequent iterations of active learning. Extensive
experiments, conducted on the challenging task of interactive satellite image
change detection, show the superiority of the proposed virtual display model
against the related work.
