  We introduce a novel interactive satellite image change detection algorithm
based on active learning. The proposed method is iterative and consists in
frugally probing the user (oracle) about the labels of the most critical
images, and according to the oracle's annotations, it updates change detection
results. First, we consider a probabilistic framework which assigns to each
unlabeled sample a relevance measure modeling how critical is that sample when
training change detection functions. We obtain these relevance measures by
minimizing an objective function mixing diversity, representativity and
uncertainty. These criteria when combined allow exploring different data modes
and also refining change detections. Then, we further explore the potential of
this objective function, by considering a reinforcement learning approach that
finds the best combination of diversity, representativity and uncertainty as
well as display-sizes through active learning iterations, leading to better
generalization as shown through experiments in interactive satellite image
change detection.
