Vehicle　detection　based　on　very　high-resolution　(VHR)　remote　sensing　images　is　beneficial　in　many　fields　such　as　military　surveillance,　traffic　control,　and　social/economic　studies.　However,　intricate　details　about　the　vehicle　and　the　surrounding　background　provided　by　VHR　images　require　sophisticated　analysis　based　on　massive　data　samples,　though　the　number　of　reliable　labeled　training　data　is　limited.　In　practice,　data　augmentation　is　often　leveraged　to　solve　this　conflict.　The　traditional　data　augmentation　strategy　uses　a　combination　of　rotation,　scaling,　and　flipping　transformations,　etc.,　and　has　limited　capabilities　in　capturing　the　essence　of　feature　distribution　and　proving　data　diversity.　In　this　study,　we　propose　a　learning　method　named　Vehicle　Synthesis　Generative　Adversarial　Networks　(VS-GANs)　to　generate　annotated　vehicles　from　remote　sensing　images.　The　proposed　framework　has　one　generator　and　two　discriminators,　which　try　to　synthesize　realistic　vehicles　and　learn　the　background　context　simultaneously.　The　method　can　quickly　generate　high-quality　annotated　vehicle　data　samples　and　greatly　helps　in　the　training　of　vehicle　detectors.　Experimental　results　show　that　the　proposed　framework　can　synthesize　vehicles　and　their　background　images　with　variations　and　different　levels　of　details.　Compared　with　traditional　data　augmentation　methods,　the　proposed　method　significantly　improves　the　generalization　capability　of　vehicle　detectors.　Finally,　the　contribution　of　VS-GANs　to　vehicle　detection　in　VHR　remote　sensing　images　was　proved　in　experiments　conducted　on　UCAS-AOD　and　NWPU　VHR-10　datasets　using　up-to-date　target　detection　frameworks.