  The lack of object-level annotations poses a significant challenge for object
detection in remote sensing images (RSIs). To address this issue, active
learning (AL) and semi-supervised learning (SSL) techniques have been proposed
to enhance the quality and quantity of annotations. AL focuses on selecting the
most informative samples for annotation, while SSL leverages the knowledge from
unlabeled samples. In this letter, we propose a novel AL method to boost
semi-supervised object detection (SSOD) for remote sensing images with a
teacher student network, called SSOD-AT. The proposed method incorporates an
RoI comparison module (RoICM) to generate high-confidence pseudo-labels for
regions of interest (RoIs). Meanwhile, the RoICM is utilized to identify the
top-K uncertain images. To reduce redundancy in the top-K uncertain images for
human labeling, a diversity criterion is introduced based on object-level
prototypes of different categories using both labeled and pseudo-labeled
images. Extensive experiments on DOTA and DIOR, two popular datasets,
demonstrate that our proposed method outperforms state-of-the-art methods for
object detection in RSIs. Compared with the best performance in the SOTA
methods, the proposed method achieves 1 percent improvement in most cases in
the whole AL.
