论文标题
在限制标签工作的同时，提高卫星图像中飞机检测的性能：混合活动学习
Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning
论文作者
论文摘要
地球观察行业提供了具有高空间分辨率和短时间重访时间的卫星图像。 为了允许这些图像的有效运营就业，使某些任务自动化已成为必要。 在防御域中，卫星图像上的飞机检测是分析师的宝贵工具。 在此任务上获得高性能检测器只能通过利用深度学习，从而获得大量标记数据来实现。为了获得足够高质量的标签，需要对军事专家的知识。我们提出了一种混合聚类的主动学习方法，以选择最相关的数据以标记标签，从而限制了所需的数据量并进一步改善了性能。 它结合了基于多样性和不确定性的主动学习选择方法。 对于通过细分检测的飞机检测，我们表明与其他活跃学习方法相比，该方法可以提供更好或竞争的结果。
The earth observation industry provides satellite imagery with high spatial resolution and short revisit time. To allow efficient operational employment of these images, automating certain tasks has become necessary. In the defense domain, aircraft detection on satellite imagery is a valuable tool for analysts. Obtaining high performance detectors on such a task can only be achieved by leveraging deep learning and thus us-ing a large amount of labeled data. To obtain labels of a high enough quality, the knowledge of military experts is needed.We propose a hybrid clustering active learning method to select the most relevant data to label, thus limiting the amount of data required and further improving the performances. It combines diversity- and uncertainty-based active learning selection methods. For aircraft detection by segmentation, we show that this method can provide better or competitive results compared to other active learning methods.
