Applicability and performance of some similarity metrics for automated image registration

Image registration is a key to many image processing tasks such as image fusion, image change detection, GIS overlay operations, 3D visualization etc. The task of image registration needs to become efficient and automatic to process enormous amount of remote sensing data. A number of feature and intensity based image registration techniques are in vogue. The aim of this study is to evaluate the applicability and performance of the two intensity based similarity metrics, namely mutual information and cluster reward algorithm. Image registration task has been mapped as an optimization problem. A combination of a global optimizer namely Genetic algorithm and a local optimizer namely Nelder Mead Simplex algorithm have been successfully used to search registration parameters from the coarsest to the finest level of the image pyramid formed using wavelet transformation. For sound investigations, registration of remote sensing images acquired with varied spatial, spectral characteristics from the ASTER sensor have been considered. The image registration experiments suggest that both the similarity metrics have the capability of successfully registering the images with high accuracy and efficiency. In general, mutual information has yielded more accurate results than cluster reward algorithm.