Image Registration using Discrete Cosine Transform and Normalized Cross Correlation

In recent years, the accelerated growth in the field of computer vision, image fusion, medical imaging, military automatic target recognition, remote cartography and astrophotography has established the need for the development of good image registration technique for the efficient retrieval of interest point area. Image registration is the process of geometrically aligning one image to another image of the same scene taken from different viewpoints or by different sensors. The idea is to transform different sets of data into one coordinate system. It is a fundamental image processing technique and is very useful inintegrating information from different sensors, finding changes in images taken at different timesand inferring threedimensional information from stereo images. Registration involves finding out area of interest by comparing the unregistered image with source image and finding the part that has highest similarity matching.This paper presents the image registration techniques based on extracting interest point area of satellite images using Discrete Cosine Transform and normalized cross correlation. The proposed algorithm is worked over various sizes of satellite images such as 256X256, 1024X1024 etc. The root mean square error is used as similarity measure. The experiment results show that the proposed algorithm can successfully process local distortion in highresolution satellite images. The comparative study shows that DCT is faster and gives more accurate results. General Terms Algorithm, Experimentation, results, satellite images