Generative　adversarial　networks　(GANs)　have　achieved　many　excellent　results　in　hyperspectral　image　(HSI)　classification　in　recent　years,　as　GANs　can　effectively　solve　the　dilemma　of　limited　training　samples　in　HSI　classification.　However,　due　to　the　class　imbalance　problem　of　HSI　data,　GANs　always　associate　minority-class　samples　with　fake　label.　To　address　this　issue,　we　first　propose　a　semisupervised　generative　adversarial　network　incorporating　a　transformer,　called　HyperViTGAN.　The　proposed　HyperViTGAN　is　designed　with　an　external　semisupervised　classifier　to　avoid　self-contradiction　when　the　discriminator　performs　both　classification　and　discrimination　tasks.　The　generator　and　discriminator　with　skip　connection　are　utilized　to　generate　HSI　patches　by　adversarial　learning.　The　proposed　HyperViTGAN　captures　semantic　context　and　low-level　textures　to　reduce　the　loss　of　critical　information.　In　addition,　the　generalization　ability　of　the　HyperViTGAN　is　improved　through　the　use　of　data　augmentation.　Experimental　results　on　three　well-known　HSI　datasets,　Houston　2013,　Indian　Pines　2010,　and　Xuzhou,　show　that　the　proposed　model　achieves　competitive　HSI　classification　performance　in　comparison　with　the　current　state-of-the-art　classification　models.