Hyperspectral　image　(HSI)　classification　is　an　essential　task　in　remote　sensing,　but　its　performance　is　greatly　affected　by　limited　labeled　samples.　Currently,　generative　adversarial　network　(GAN)-based　methods　can　generate　the　virtue　samples　to　augment　the　training　set.　However,　with　limited　labeled　data,　GANs　perform　poorly　in　capturing　features　during　sample　generation.　Very　few　relation　networks　(RNs)　and　few-shot　learning　(FSL)　methods　considered　data　augmentation　to　enhance　performance.　To　address　this　challenge,　we　propose　FSHyperRGAN,　a　few-shot　HSI　classification　method　based　on　relational　GAN,　which　uses　GANs　to　augment　the　training　samples　for　RNs,　while　leveraging　relation　feature　extraction　to　guide　the　generation　of　specific　class　samples.　FSHyperRGAN　comprises　four　modules:　a　data　processing　module　converting　the　HSI　data　to　1-D　and　3-D　features,　an　adversarial　generation　(AG)　module　synthesizing　virtual　samples　conditioned　on　labels,　a　data　embedding　and　reconstruction　(DER)　module　encoding　latent　spaces　for　accurate　sample　reconstruction　while　preserving　category　characteristics,　and　a　relation　computation　(RC)　module　computing　relation　scores　across　generated,　reconstructed,　and　original　samples.　In　addition,　a　relational　feature　matching　scheme　is　also　applied,　which　can　use　virtual　samples　to　guide　classification.　Two　FSHyperRGAN　frameworks　are　designed,　1D-FSHyperRGAN　and　3D-FSHyperRGAN,　which　can　be　utilized　for　1-D　spectral　or　3-D　spatial-spectral　classification.　Experiments　on　widely　used　HSI　datasets　illustrate　that　the　proposed　method　outperforms　several　state-of-the-art　methods　and　achieves　overall　accuracies　of　92.64%,　86.82%,　83.64%,　and　84.57%　on　KSC,　PaviaU,　Houston,　and　WHU-Hi-HongHu　datasets,　respectively.　©　1980-2012　IEEE.