论文标题
卫星图像变更检测的基于加固的节俭学习检测
Reinforcement-based frugal learning for satellite image change detection
论文作者
论文摘要
在本文中，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。提出的方法是迭代的，并询问用户（Oracle）有关目标更改的问题，并根据Oracle的响应更新更改检测。我们考虑一个概率框架，该框架将分配给每个未标记的样本一个相关度量模型，以建模训练更改检测功能时该样本的关键程度。这些相关性措施是通过最大程度地降低目标函数混合多样性，代表性和不确定性来获得的。这些标准合并后允许探索不同的数据模式并完善变更检测。为了进一步探索该目标功能的潜力，我们考虑了一种强化学习方法，该方法通过主动学习迭代找到了多样性，代表性和不确定性的最佳组合，从而通过在交互式卫星图像变化检测中进行实验来证实，从而使得更好地泛化。
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.
