This letter presents a postprocessing algorithm for a kernel sparse representation (KSR)-based hyperspectral image classifier, which is based on the integration of spatial and spectral information. A pixelwise KSR is first used to find the sparse coefficient vectors of the hyperspectral image. Then, a sparsity concentration index (SCI) rule-guided semilocal spatial graph regularization (SSG), called SSG+SCI, is proposed to determine refined sparse coefficient vectors that promote spatial continuity within each class. Finally, these refined coefficient vectors are used to obtain the final classification map. Compared with previous approaches based on similar spatial-spectral postprocessing strategies, SSG+SCI clearly outperforms their results in terms of accuracy and the number of training samples, as it is demonstrated with two real hyperspectral images.