  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.
