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Vehicle detection in remote sensing images has attracted increasing interestin recent years. However, its detection ability is limited due to lack ofwell-annotated samples, especially in densely crowded scenes. Furthermore,since a list of remotely sensed data sources is available, efficientexploitation of useful information from multi-source data for better vehicledetection is challenging. To solve the above issues, a multi-source activefine-tuning vehicle detection (Ms-AFt) framework is proposed, which integratestransfer learning, segmentation, and active classification into a unifiedframework for auto-labeling and detection. The proposed Ms-AFt employs afine-tuning network to firstly generate a vehicle training set from anunlabeled dataset. To cope with the diversity of vehicle categories, amulti-source based segmentation branch is then designed to construct additionalcandidate object sets. The separation of high quality vehicles is realized by adesigned attentive classifications network. Finally, all three branches arecombined to achieve vehicle detection. Extensive experimental results conductedon two open ISPRS benchmark datasets, namely the Vaihingen village and Potsdamcity datasets, demonstrate the superiority and effectiveness of the proposedMs-AFt for vehicle detection. In addition, the generalization ability of Ms-AFtin dense remote sensing scenes is further verified on stereo aerial imagery ofa large camping site.

Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network