Nonsubsampled contourlet transform combined with genetic algorithms for registration of satellite imaging

Digital image registration is a process by which the most accurate match is determined between two images which may have been taken at the same or different viewpoints. The registration process determines the optimal transformation which will align the two images. This has applications in many fields as diverse as medical images analysis, pattern matching and computer vision for robotics, as well as remotely sensed data processing. A wide range of registration techniques has been developed for many different types of applications and data. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. Therefore, in this work, we describe an efficient algorithm of image registration that uses genetic algorithms within a multi-resolution framework based on the wavelet and the nonsubsampled contourlet transform (NSCT). We combine local search methods with global ones balancing exploration and exploitation, to speed up the search of the best transformation parameters. Experimental results show that the NSCT is a promising method for registration of satellite images compared to the wavelet transform.