Robust and Precise Registration of Oblique Images Based on Scale-Invariant Feature Transformation Algorithm

The automatic registration of oblique images taken at different viewpoints remains a challenge until today. Based on scale-invariant feature transformation (SIFT) algorithm, a robust and accurate weighted least square matching (LSM) (SIFT/LSM) method modeled using 2-D projective transformation is proposed for highly accurate registration of oblique images. Normalized cross correlation (NCC) metric modified by an adaptive scale and orientation of SIFT features (SIFT/NCC) is proposed to obtain a good initial estimation for the SIFT/LSM. For practical use, image matching is implemented using a coarse-to-fine multistage strategy by sequentially incorporating the standard SIFT algorithm, SIFT/NCC, and SIFT/LSM. Experiments conducted on oblique images of real-world scenes demonstrate the feasibility of the proposed approach.