  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.
