Remote Sensing Image Registration Based on Retrofitted SURF Algorithm and Trajectories Generated From Lissajous Figures

In this letter, we propose a novel remote sensing image registration method by optimizing the Speeded Up Robust Features (SURF) and developing a new similarity measure function based on trajectories generated from Lissajous figures. Compared with SURF which has a low feature-matching rate in some complex cases, the retrofitted SURF algorithm is more robust and accurate. The algorithm greatly improves the correct matching rate to over 80%. Furthermore, the recognition capability of the similarity measure is enhanced by using a trajectory disturbance strategy, which is a significant displacement in the trajectory induced by a minor error of the transformation parameters. Experiments show the promising performance of the proposed image registration method.