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Registration and fusion of multispectral images using a new control point assessment method derived from optical flow ideas

A new method of automatically determining control points for registration and fusion of multispectral images is presented. This method was motivated by the question of whether it is possible to computationally assess the quality of optical flow estimates at various points throughout the image without knowing the true flow field. Somewhat surprisingly the answer is yes and is determined by the norm of the least squares operator associated with the (windowed) optical flow equations. This approach has several advantages. First it shows the danger of using the condition number of the optical flow equations to measure the reliability of the computed flow. Second the method isolates points in the image corresponding to maximum reliability. These points in turn can be used as control points for registration and fusion without actually computing the optical flow and indeed only require a single frame for computation. Since this computation only requires a few operations per pixel it is very fast. The control points are defined as the minima of the norm of the least squares operator and as such enjoy a great deal of invariance with respect to the regional intensity changes seen in multispectral images. For this reason they are ideal for multispectral registration. A multiscale version of this method has been developed that allows a coarse to fine control point decomposition for suppressing the negative effects of noise and clutter. Various applications are presented demonstrating the utility of this approach for real world images including multispectral satellite images and dual spectral IR image sequences. For the latter sequence we were able to obtain subpixel motion estimates that were accurate to within one percent of the true motion.