Evaluating performance of automatic techniques for subpixel registration of remotely sensed imagery

We describe two approaches of systematic performance assessment of a specific image registration algorithm. One approach involves generating radiometrically different synthetic images by convolving one of them with a point-spread function, while the other consists of registration of three or more images to obtain multiple estimates of registration parameters. We present experimental results that indicate that different-radiometry synthetic data is more difficult to register and so it provides better testing than same-radiometry data used in our previous work. They also show that the multiple-estimate methodology, that we call triangulation, may be used not only to measure self-consistency of a given registration algorithm, but also to obtain estimates of ground truth information for images for which, if available at all, the ground truth is known only approximately.