Domain Adaptation with Contrastive Learning for Object Detection in
Satellite Imagery
Abstract
State-of-the-art object detection methods applied to satellite and drone
imagery largely fail to identify small and dense objects. One reason is
the high variability of content in the overhead imagery due to the
terrestrial region captured and the high variability of acquisition
conditions. Another reason is that the number and size of objects in
aerial imagery are very different than in the consumer data. In this
work, we propose a small object detection pipeline that improves the
feature extraction process by spatial pyramid pooling, cross-stage
partial networks, heatmap-based region proposal network, and object
localization and identification through a novel image difficulty score
that adapts the overall focal loss measure based on the image
difficulty. Next, we propose novel contrastive learning with progressive
domain adaptation to produce domain-invariant features across aerial
datasets using local and global components. We show we can alleviate the
degradation of object identification in previously unseen datasets. We
create a first-ever domain adaptation benchmark using contrastive
learning for the object detection task in highly imbalanced satellite
datasets with significant domain gaps and dominant small objects from
existing satellite benchmarksâ\euro”the proposed method results in up
to a 7.4% increase in mAP performance measure over the best
state-of-art.Â