Abstract-Image registration is the process by which we determine a transformation that provides the most accurate match between two images. The search for the matching transformation can be automated with the use of a suitable metric, but it can be very time-consuming and tedious. In this paper, we introduce a registration algorithm that combines a simple yet powerful search strategy based on a stochastic gradient with two similarity measures, correlation and mutual information, together with a wavelet-based multiresolution pyramid. We limit our study to pairs of images, which are misaligned by rotation and/or translation, and present two main results. First, we demonstrate that in our application mutual information may be better suited for sub-pixel registration as it produces consistently sharper optimum peaks than correlation. Then, we show that the stochastic gradient search combined with either measure produces accurate results when applied to synthetic, as well as multitemporal or multisensor collections of satellite data. Mutual information is generally found to optimize with one-third the number of iterations required by correlation. Results also show that a multiresolution implementation of the algorithm yields significant improvements in terms of both speed and robustness over a single-resolution implementation.

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Wavelet-based image registration has previously been proposed by the authors. In previous work, maxima obtained from orthogonal Daubechies filters as well as from Simoncelli steerable filters were utilized and compared to register images with a multi-resolution correlation technique. Previous comparative studies between both types of filters have shown that the accuracy obtained with orthogonal filters seemed to degrade very quickly for large rotations and large amounts of noise, while results obtained with steerable filters appeared much more stable under these conditions. In other studies based on the use of mutual information for image registration, several authors have shown that maximizing mutual information enables one to reach sub-pixel registration accuracy. In this work, we are utilizing Simoncelli steerable filters to provide the basic data from which mutual information is maximized and we are applying this method to remotely sensed imagery.