论文标题
节俭学习虚拟示例的标签卫星图像更改检测
Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection
论文作者
论文摘要
在本文中，我们设计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。所提出的框架是迭代的，依赖于问答模型，该模型询问Oracle（用户）有关最有用的显示（关键图像的子集）的问题，并且根据用户的回答，更新更新了检测。我们的框架的贡献在于一个新颖的显示模型中，该模型选择了最具代表性和多样化的虚拟示例，这些示例会对学习的变化检测功能质疑，从而在随后的主动学习的后续迭代中产生了高度歧视的功能。对交互式卫星图像变化检测的挑战性任务进行的广泛实验表明了所提出的虚拟显示模型与相关工作的优越性。
In this paper, we devise a novel interactive satellite image change detection algorithm based on active learning. The proposed framework is iterative and relies on a question and answer model which asks the oracle (user) questions about the most informative display (subset of critical images), and according to the user's responses, updates change detections. The contribution of our framework resides in a novel display model which selects the most representative and diverse virtual exemplars that adversely challenge the learned change detection functions, thereby leading to highly discriminating functions in the subsequent iterations of active learning. Extensive experiments, conducted on the challenging task of interactive satellite image change detection, show the superiority of the proposed virtual display model against the related work.
