  In defense-related remote sensing applications, such as vehicle detection on
satellite imagery, supervised learning requires a huge number of labeled
examples to reach operational performances. Such data are challenging to obtain
as it requires military experts, and some observables are intrinsically rare.
This limited labeling capability, as well as the large number of unlabeled
images available due to the growing number of sensors, make object detection on
remote sensing imagery highly relevant for self-supervised learning. We study
in-domain self-supervised representation learning for object detection on very
high resolution optical satellite imagery, that is yet poorly explored. For the
first time to our knowledge, we study the problem of label efficiency on this
task. We use the large land use classification dataset Functional Map of the
World to pretrain representations with an extension of the Momentum Contrast
framework. We then investigate this model's transferability on a real-world
task of fine-grained vehicle detection and classification on Preligens
proprietary data, which is designed to be representative of an operational use
case of strategic site surveillance. We show that our in-domain self-supervised
learning model is competitive with ImageNet pretraining, and outperforms it in
the low-label regime.
