In machine learning, the term active learning regroups techniques that aim at selecting the most useful data to label from a large pool of unlabelled examples. While supervised deep learning techniques have shown to be increasingly efficient on many applications, they require a huge number of labelled examples to reach operational performances. Therefore, the labelling effort linked to the creation of the datasets required is also increasing. When working on defense-related remote sensing applications, labelling can be challenging due to the large areas covered and often requires military experts who are rare and whose time is primarily dedicated to operational needs. Limiting the labelling effort is thus of utmost importance. This study aims at reviewing the most relevant active learning techniques to be used for object detection on very high resolution imagery and shows an example of the value of such techniques on a relevant operational use case: aircraft detection.

中文翻译：

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主动学习用于高分辨率卫星图像中的目标检测

在机器学习中，术语“主动学习”将旨在从大量未标记示例中选择最有用的数据进行标记的技术重新组合。尽管监督式深度学习技术在许多应用程序上显示出越来越高的效率，但它们需要大量带有标签的示例才能达到操作性能。因此，与创建所需数据集相关的标记工作也在增加。在国防相关的遥感应用中进行工作时，由于覆盖区域较大，因此标注可能具有挑战性，并且常常需要军事专家，这些专家很少，而且他们的时间主要用于作战需求。因此，限制贴标签的工作至关重要。