Vehicle detection in remote sensing images has attracted increasing interest in recent years. However, its detection ability is limited due to lack of well-annotated samples, especially in densely crowded scenes. Furthermore, since a list of remotely sensed data sources is available, efficient exploitation of useful information from multi-source data for better vehicle detection is challenging. To solve the above issues, a multi-source active fine-tuning vehicle detection (Ms-AFt) framework is proposed, which integrates transfer learning, segmentation, and active classification into a unified framework for auto-labeling and detection. The proposed Ms-AFt employs a fine-tuning network to firstly generate a vehicle training set from an unlabeled dataset. To cope with the diversity of vehicle categories, a multi-source based segmentation branch is then designed to construct additional candidate object sets. The separation of high quality vehicles is realized by a designed attentive classifications network. Finally, all three branches are combined to achieve vehicle detection. Extensive experimental results conducted on two open ISPRS benchmark datasets, namely the Vaihingen village and Potsdam city datasets, demonstrate the superiority and effectiveness of the proposed Ms-AFt for vehicle detection. In addition, the generalization ability of Ms-AFt in dense remote sensing scenes is further verified on stereo aerial imagery of a large camping site.

近年来，遥感图像中的车辆检测引起了越来越多的兴趣。但是，由于缺少注释良好的样本，因此其检测能力受到限制，尤其是在人群密集的场景中。此外，由于可获得遥感数据源的列表，因此从多源数据有效利用有用信息以更好地进行车辆检测具有挑战性。为了解决上述问题，提出了一种多源主动微调车辆检测（Ms-AFt）框架，该框架将转移学习，分段和主动分类集成到一个统一的自动标记和检测框架中。提出的Ms-AFt使用微调网络首先从未标记的数据集中生成车辆训练集。为了应对车辆类别的多样性，然后设计一个基于多源的分段分支，以构造其他候选对象集。通过精心设计的分类网络可以实现高质量车辆的分离。最后，将所有三个分支合并以实现车辆检测。在两个开放式ISPRS基准数据集（Vaihingen村和波茨坦市数据集）上进行的大量实验结果证明了拟议的Ms-AFt在车辆检测中的优越性和有效性。此外，在大型露营地的立体航拍图像上进一步验证了Ms-AFt在密集遥感场景中的泛化能力。将三个分支机构全部合并以实现车辆检测。在两个开放式ISPRS基准数据集（Vaihingen村和波茨坦市数据集）上进行的大量实验结果证明了拟议的Ms-AFt在车辆检测中的优越性和有效性。此外，在大型露营地的立体航拍图像上进一步验证了Ms-AFt在密集遥感场景中的泛化能力。将三个分支机构全部合并以实现车辆检测。在两个开放式ISPRS基准数据集（Vaihingen村和波茨坦市数据集）上进行的大量实验结果证明了拟议的Ms-AFt在车辆检测中的优越性和有效性。此外，在大型露营地的立体航拍图像上进一步验证了Ms-AFt在密集遥感场景中的泛化能力。