Label-Efficient Display-Size Selection for Satellite Image Change Detection

We introduce a novel label-efficient satellite image change detection algorithm based on active and reinforcement learning. The proposed method is interactive and consists in frugally probing the user (oracle) about the labels of the most critical images, and according to the oracle’s annotations, updates change detection results. First, we consider a probabilistic model 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 uncertainty, diversity and representativity; when combined, these three criteria 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 these three criteria together with display-sizes through active learning iterations, leading to better generalization as shown through experiments in interactive satellite image change detection.