Spectral　similarity　refers　to　that　there　are　many　pixels　with　similar　spectrum　in　a　single　hyperspectral　image.　In　this　paper,　we　propose　a　spectral　similarity-based　super　resolution　method　for　hyperspectral　images.　In　our　method,　the　extra　information　exploited　from　structural　self-similarity　which　widely　exists　in　remote　sensing　images　is　used　to　promote　the　spatial　resolution.　The　principal　component　analysis　is　used　to　reduce　the　spectral　dimension　for　increasing　the　computational　efficiency　according　to　the　inherent　low　dimensionality　of　hyperspectral　images,　and　the　spectral　similarity　is　used　to　construct　spectral　regularization　for　ensuring　the　accuracy　of　the　spectrum　in　the　reconstructed　image.　Our　method　can　achieve　accurately　reconstructed　results　as　well　as　high　computational　efficiency,　when　we　extend　the　singleband　super　resolution　method　to　the　hyperspectral　image　with　hundreds　of　bands.　Experimental　results　demonstrate　that　our　method　can　improve　the　spatial　resolution　more　effectively　and　reconstruct　the　spectrum　more　accurately　than　the　bicubic　interpolation　and　the　sparse　representation　and　spectral　regularization　method　(SRSRM).