Tea　is　an　important　economic　plant,　which　is　widely　cultivated　in　many　countries,　particularly　in　China.　Accurately　mapping　tea　plantations　is　crucial　in　the　operations,　management,　and　supervision　of　the　growth　and　development　of　the　tea　industry.　We　propose　an　object-based　convolutional　neural　network　(CNN)　to　extract　tea　plantations　from　very　high　resolution　remote　sensing　images.　Image　segmentation　was　performed　to　obtain　image　objects,　while　a　fine-tuned　CNN　model　was　used　to　extract　deep　image　features.　We　conducted　feature　selection　based　on　the　Gini　index　to　reduce　the　dimensionality　of　deep　features,　and　the　selected　features　were　then　used　for　classifying　tea　objects　via　a　random　forest.　The　proposed　method　was　first　applied　to　Google　Earth　images　and　then　transferred　to　GF-2　satellite　images.　We　compared　the　proposed　classification　with　existing　methods:　Object-based　classification　using　random　forest,　Mask　R-CNN,　and　object-based　CNN　without　fine-tuning.　The　results　show　the　proposed　method　achieved　a　higher　classification　accuracy　than　other　methods　and　produced　smaller　over-　and　under-classification　geometric　errors　than　Mask　R-CNN　in　terms　of　shape　integrity　and　boundary　consistency.　The　proposed　approach,　trained　using　Google　Earth　images,　achieved　comparable　results　when　transferring　to　the　classification　of　tea　objects　from　GF-2　images.　We　conclude　that　the　proposed　method　is　effective　for　mapping　tea　plantations　using　very　high-resolution　remote　sensing　images　even　with　limited　training　samples　and　has　huge　potential　for　mapping　tea　plantations　in　large　areas.