  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.
