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/ .

翻译：近期研究采用无监督域自适应目标检测（UDAOD）方法以弥合遥感（RS）图像中的域间差异。然而，UDAOD方法通常假定在域自适应过程中可访问源域数据。由于遥感数据隐私性及传输困难，该设定在实际应用中往往不切实际。为解决这一挑战，我们提出了一种适用于遥感图像的实用化无源目标检测（SFOD）设定，旨在仅利用源预训练模型实现目标域自适应。我们针对遥感图像提出了一种新型SFOD方法，包含两个部分：扰动域生成与对齐。所提出的多级扰动通过依据颜色与风格偏差在图像层和特征层扰动域变异特征，以简洁高效的形式构造扰动域。所提出的多级对齐通过师生网络计算扰动域与目标域之间的特征一致性和标签一致性，并引入特征原型蒸馏以抑制伪标签噪声。通过要求检测器在扰动域与目标域中保持一致性，迫使检测器聚焦于域不变特征。三项合成到真实场景实验与三项跨传感器实验的广泛结果验证了本方法的有效性——该方法无需访问源域遥感图像。此外，计算机视觉数据集上的实验表明，本方法亦可拓展至其他领域。我们的代码将发布于：https://weixliu.github.io/。