  Recent studies have used unsupervised domain adaptive object detection
(UDAOD) methods to bridge the domain gap in remote sensing (RS) images.
However, UDAOD methods typically assume that the source domain data can be
accessed during the domain adaptation process. This setting is often
impractical in the real world due to RS data privacy and transmission
difficulty. To address this challenge, we propose a practical source-free
object detection (SFOD) setting for RS images, which aims to perform target
domain adaptation using only the source pre-trained model. We propose a new
SFOD method for RS images consisting of two parts: perturbed domain generation
and alignment. The proposed multilevel perturbation constructs the perturbed
domain in a simple yet efficient form by perturbing the domain-variant features
at the image level and feature level according to the color and style bias. The
proposed multilevel alignment calculates feature and label consistency between
the perturbed domain and the target domain across the teacher-student network,
and introduces the distillation of feature prototype to mitigate the noise of
pseudo-labels. By requiring the detector to be consistent in the perturbed
domain and the target domain, the detector is forced to focus on
domaininvariant features. Extensive results of three synthetic-to-real
experiments and three cross-sensor experiments have validated the effectiveness
of our method which does not require access to source domain RS images.
Furthermore, experiments on computer vision datasets show that our method can
be extended to other fields as well. Our code will be available at:
https://weixliu.github.io/ .
