To solve the problems of lost details and added noise in the previous sparse representation image super-resolution, an improved feature extraction algorithm was proposed to improve the image Super-Resolution Reconstruction (SRR) effect. The Gaussian filter was replaced by a symmetric nearest neighbor filter to speed up image super-resolution, and the problem of dictionary learning in the feature space was solved. First, sample training images were generated based on the remote sensing image degradation model, and high-low resolution images were divided into image patches sized 7×7. Then, a high-low resolution joint dictionary mapping matrix was generated after the dictionary was trained and updated. Finally, image super-resolution reconstruction was performed in sparse representation. Experimental results revealed that the proposed method reconstructed a higher-quality super-resolution image in less time. Simultaneously, as compared with the image obtained with the most advanced sparse representation super-resolution algorithm, the SRR resulting image contained more texture details of ground objects. In addition, the peak signal-to-noise ratio and structural similarity index measure were increased by approximately 1.7 dB and 0.016, respectively. Conclusion: The improved sparse representation SRR algorithm can effectively improve the SRR effect of remote sensing images and reduce the super-resolution reconstruction time.