In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images, and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art Scale-Invariant Feature Transform (SIFT) features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional priori information, including a robust feature location map and platform imaging parameters. Numerical simulation results show that the proposed scheme increases position repetitiveness by $\mathrm{34}\mathrm{\%}$, and can speed up the overall image registration procedure by a factor of $\mathrm{7.47}$ while maintaining the accuracy of the image registration performance.