Based on the analyses of the interpixel correlation in the hyperspectral imagery, a spatial correlation constrained simultaneous subspace pursuit (SCCSSP) method is proposed. The method uses a block-processing strategy to divide the whole hyperspectral imagery into several blocks. In each block, the spatial correlation information is added to improve the accuracy of endmember selection, and ensures that the estimated endmember set is optimal to the current hyperspectral image residuals. The endmembers picked in each block is associated as the endmember sets of the whole hyperspectral imagery. Finally, the abundances are estimated by the nonnegative least squares method with the obtained end member sets. The results of simulated images experiment show that the proposed method can obtain higher signal reconstruction error under the same condition, and the time of unmixing operation is lower than the convex optimization algorithms. In the real images experiment, this method has the lowest sparsity of the abundance images, and is second only to the SUnSAL-TV algorithm in image reconstruction error. In addition, the reconstructed images obtained by this method obtain better visual effects. To sum up, experimental results on both simulated images and real images indicate that the hyperspectral unmixing accuracy of the SCCSSP algorithm is higher than that of the traditional methods.