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 . The proposed Ms-AFt employs a fine-tuning network to 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 . For more information, please visit .

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Keywords : vehicle - detection - network - fine - multi -

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