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Image registration is a fundamental image processing task to match and align physically two images which could have been imaged by different sensors, view angles and/or at different times. While registering the satellite images, there are several unique challenges like cloud pixels, noise in the images, systematic errors, multispectral images, terrain induced distortions, etc. Through image registration techniques, we are finding a proper geometric transform between two images that can align corresponding points in them. It is the foundation of applications, such as image fusion, medical image processing, remote sensing and threedimensional (3D) image reconstructions. Remote sensing is one of the fields that have benefited most from image registration technique for stitching the reference and sensed images to get a larger view of the scene. The paper presents the Least Square-Support Vector Machine (LS-SVM) which has very strong theoretical foundation based on statistical learning theory. The experimental results demonstrate that global deformations can be well registered by LS-SVM-based multiview image registration. [ABSTRACT FROM AUTHOR]