Hyperspectral　Images(HSIs)　are　data　containing　abundant　spatial　and　spectral　information,　which　is　collected　by　advanced　remote　sensors.　HSI　Classification　is　a　pixel-wise　classification　task　that　has　broad　prospects　in　the　era　of　science　and　technology.　In　recent　years,　the　widely　used　convolutional　neural　networks　(CNNs)　have　come　to　the　leading　place　in　HSI　Classification.　However,　the　lack　of　utilization　of　spatial　information　limits　its　further　application.　To　solve　this　issue,　we　considered　the　recently　proposed　Vision　Transformer(ViT),　which　is　modularized　structures　that　are　entirely　based　on　self-attention　mechanism.　Furthermore,　we　proposed　a　patch-wise　radially-accumulate　module　for　ViT(RA-ViT)　in　HSI　Classification.　We　evaluated　the　proposed　method　on　Indian　Pines(IP)　and　Kennedy　Space　Center(KSC)　datasets.　The　results　of　experiments　demonstrate　the　effectiveness　of　RA-ViT　with　comparison　to　current　advanced　models.　©　Published　under　licence　by　IOP　Publishing　Ltd.