Abstract: 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 single-band 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).