Hyperspectral　classification　is　an　important　technique　for　remote　sensing　image　analysis.　For　the　current　classification　methods,　limited　training　data　affect　the　classification　results.　Recently,　Conditional　Variational　Autoencoder　Generative　Adversarial　Network　(CVAEGAN)　has　been　used　to　generate　virtual　samples　to　augment　the　training　data,　which　could　improve　the　classification　performance.　To　further　improve　the　classification　performance,　based　on　the　CVAEGAN,　we　propose　a　Self-Attention-Based　Conditional　Variational　Autoencoder　Generative　Adversarial　Network　(SACVAEGAN).　Compared　with　CVAEGAN,　we　first　use　random　latent　vectors　to　obtain　more　enhanced　virtual　samples,　which　can　improve　the　generalization　performance.　Then,　we　introduce　the　self-attention　mechanism　into　our　model　to　force　the　training　process　to　pay　more　attention　to　global　information,　which　can　achieve　better　classification　accuracy.　Moreover,　we　explore　model　stability　by　incorporating　the　WGAN-GP　loss　function　into　our　model　to　reduce　the　mode　collapse　probability.　Experiments　on　three　data　sets　and　a　comparison　of　the　state-of-art　methods　show　that　SACVAEGAN　has　great　advantages　in　accuracy　compared　with　state-of-the-art　HSI　classification　methods.