Robust Image Registration Using Structure Features

Due to repetitive patterns and gray changes in the remote sensing images, feature-point-based registration methods generally fail to determine every correctly matched point. In this letter, a novel registration algorithm that uses point structure information, which includes an improved shape context in feature description and consensus graph emerging from putative matches in feature matching, is proposed. First, to obtain robust initial matching point pairs, a DAISY descriptor is combined with a shape context descriptor that is improved using 1-D Fourier transformation. Second, the final matching results are estimated using graphic transform matching based on the local structure information of the point to remove outliers from initial correspondences. Finally, experimental results demonstrate that the proposed approach, based on structure information, is robust and can improve alignment precision in particularly complex environments.