Normalized cross correlation (NCC) has been widely used to match control points (CP) in image alignment. This method will produce a lot of incorrect matches owing to the significant difference in the image intensity between multispectral image pairs, and furthermore, it is very computationally expensive to handle rotational displacement. This letter presents a method using rotation-invariant distance to match CPs; a local descriptor matrix is built to describe each CP, and fast Fourier transform is introduced to compute the rotation-invariant distance between the matrices. The computational load is sharply decreased by rotation-invariant distance compared to NCC, and furthermore, the load will remain unchanged in circumstance with arbitrary rotational angle. Experimental results indicate that the proposed method improves the match performance compared to other state-of-the-art methods in terms of correct match rate and aligning accuracy.
['Qiaoliang Li', 'Huisheng Zhang', 'Tianfu Wang']
Multispectral Image Matching Using Rotation-Invariant Distance