  In this paper, we introduce a novel interactive satellite image change
detection algorithm based on active learning. The proposed approach is
iterative and asks the user (oracle) questions about the targeted changes and
according to the oracle's responses updates change detections. 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. These relevance measures are obtained by minimizing an objective
function mixing diversity, representativity and uncertainty. These criteria
when combined allow exploring different data modes and also refining change
detections. To further explore the potential of this objective function, we
consider a reinforcement learning approach that finds the best combination of
diversity, representativity and uncertainty, through active learning
iterations, leading to better generalization as corroborated through
experiments in interactive satellite image change detection.
