2-phase GA-based image registration on parallel clusters

Genetic algorithms (GAs) are 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 present 2-phase sequential and coarse-grained parallel image registration algorithms using GAs as optimization mechanism. In its first phase, the algorithm finds a small set of goad solutions using low-resolution Versions of the images. Based on these candidate low-resolution solutions, the algorithm uses the full resolution image data to refine the final registration results in the second phase. Experimental results are presented and revealed that our algorithms yield very accurate registration results for LandSat Thematic Mapper images, and the parallel algorithm scales quite well on the Beowulf parallel cluster. (C) 2001 Elsevier Science B.V. All rights reserved.