Sub-pixel accuracy in registration of synthetic aperture radar (SAR) images is still a challenging task in remote sensing applications. Speeded Up Robust Feature (SURF) is one of the most popularly used method for feature detection and description of SAR images. But using SURF alone in registration cannot give accurate matching in corresponding features, as it contains many wrong correspondences called outliers. RANSAC (Random Sample Consensus) an outlier removal technique is used to remove

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... e outliers. Even then some outliers still exist which degrade the registration quality. In this paper, a novel algorithm is proposed to remove those remaining outliers by limiting the RMSE to less than 0.5 in registration process. Firstly, SURF based feature matching is performed between image pairs to get the corresponding features, then RANSAC is used to remove most of the outliers obtained from SURF feature matching. Then, the proposed method is applied to still refine the matched features obtained after RANSAC.