  We introduce in this paper a novel active learning algorithm for satellite
image change detection. The proposed solution is interactive and based on a
question and answer model, which asks an oracle (annotator) the most
informative questions about the relevance of sampled satellite image pairs, and
according to the oracle's responses, updates a decision function iteratively.
We investigate a novel framework which models the probability that samples are
relevant; this probability is obtained by minimizing an objective function
capturing representativity, diversity and ambiguity. Only data with a high
probability according to these criteria are selected and displayed to the
oracle for further annotation. Extensive experiments on the task of satellite
image change detection after natural hazards (namely tornadoes) show the
relevance of the proposed method against the related work.
