The　remote　sensing　data　is　difficult　to　collect　and　lack　of　diversity,　which　extremely　limits　the　performance　of　object　detection　on　remote　sensing　images.　In　this　paper,　a　multi-branch　conditional　generative　adversarial　network　(MCGAN)　is　proposed　to　augment　data　for　object　detection　in　optical　remote　sensing　images,　which　is　the　first　GANs-based　data　augmentation　framework　proposed　for　this　topic.　We　use　MCGAN　to　generate　the　diverse　objects　based　on　the　existing　remote　sensing　datasets.　The　multi-branch　dilated　convolution　and　the　classification　branch　are　adopted　into　MCGAN　to　help　the　generator　to　generate　the　diverse　and　high-quality　images.　Meanwhile,　an　adaptive　samples　selection　strategy　based　on　the　Faster　R-CNN　is　proposed　to　select　the　samples　for　data　augmentation　from　the　objects　generated　by　MCGAN,　which　can　ensure　the　quality　of　new　augmented　training　sets　and　improve　the　diversity　of　samples.　Experiments　based　on　NWPU　VHR-10　and　DOTA　show　that　the　objects　generated　by　MCGAN　have　the　higher　quality　compared　with　the　objects　generated　by　WGAN　and　LSGAN.　And　the　mean　average　precision　detected　by　the　state-of-the-art　object　detection　models　used　in　the　experiments　has　the　satisfactory　improvement　after　the　MCGAN　based　data　augmentation,　which　indicates　that　data　augmentation　by　MCGAN　can　effectively　improve　the　accuracy　of　remote　sensing　images　object　detection.　(C)　2019　Elsevier　B.V.　All　rights　reserved.