Introduction Many applications in remote sensing rely on image registration. These include three-dimensional mapping of the land and sea surface, identifying and mapping different types of land use, measuring and analyzing natural and agricultural vegetation, matching stereo images to recover shape for autonomous navigation, and aligning images from different medical modalities for diagnosis. With the dramatic increase in data volumes and types of sensors, image registration became also crucial for content-based retrieval of remote sensing data and image data from large data repositories. Image registration is to determine the transformation between a newly sensed image, called input image, and a reference image. The complexity of the search and similarity measurement in image registration could be computationally intensive.
Computational time becomes even more critical with the current increase in data.
As a result, high performance image registration is needed. The fraction of the functions for GA itself is very small. The GA operations including selection and reproduction are relative small compared to the evaluation operations. In global GA, a master node keeps track of all genetic-related processes except for the evaluation process of the chromosomes. The evaluations of the fitness of the chromosomes can be spread over the processing nodes.