GA-Based Parallel Image Registration on Parallel Clusters

Genetic Algorithms (GAs) have been known to be robust for search and optimization problems. Image registration can take advantage of the robustness of GAs in finding best transformation between two images, of the same location with slightly different orientation, produced by moving spaceborne remote sensing instruments. In this paper, we have developed sequential and coarse-grained parallel image registration algorithms using GA as an optimization mechanism. In its first phase the algorithm finds a small set of good solutions using low-resolution versions of the images. Based on the results from the first phase, the algorithm uses full resolution image data to refine the final registration results in the second phase. Experimental results are presented and we found that our algorithms yield very accurate registration results and the parallel algorithms scales quite well on the Beowulf parallel cluster.