Combining local affine frames and SIFT for remote sensing image registration

Abstract

Local invariant features such as scale invariant feature transform (SIFT) have received considerable attention in recent years. Despite its tremendous success in computer vision applications, SIFT matching alone is not sufficient for remote sensing image registration because of low detection repeatablility and nonlinear intensity changes. In this paper, we introduce a remote sensing image registration algorithm that combines local affine frames (LAF) together with SIFT matching. Firstly, distinctive SIFT keypoints and maximally stable extremal regions (MSER) are detected independently in the reference image and the sensed image. Contrast reversal invariant SIFT descriptor is constructed for describing texture patches around SIFT keypoints and shape descriptor defined in LAF is constructed for describing MSER contour. Nearest neighbor distance ratio matching with confidence measurement is then adopted to match both descriptors. Tentative correspondences are ranked according to their confidence measurements. Finally, random sample consensus (RANSAC) is performed in the top ranked matched features to obtain a global set of transform parameters. Experimental results demonstrate the robustness and accuracy of the proposed method.