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.

我们介绍了一种基于主动学习的新型交互式卫星图像变化检测算法，该算法通过迭代的方式在用户（推荐系统）上进行询问，根据用户的注释更新变化检测结果，我们使用概率框架来为每个未标记的样本分配相关性度量，通过最小化包含多样性、代表性和不确定性的目标函数来获得这些相关性度量，使用强化学习方法探索多样性、代表性和不确定性的最佳组合以及显示尺寸，通过交互式卫星图像变化检测的实验证明能够获得更好的泛化效果。

基于强化学习的节俭卫星图像变化检测显示尺寸选择