Combining local affine frames and SIFT for remote sensing image registration

Hao Sun; Cheng Wang

doi:10.1117/12.832498

30 October 2009 Combining local affine frames and SIFT for remote sensing image registration

Hao Sun, Cheng Wang

Proceedings Volume 7494, MIPPR 2009: Multispectral Image Acquisition and Processing; 74941B (2009) https://doi.org/10.1117/12.832498
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Hao Sun and Cheng Wang "Combining local affine frames and SIFT for remote sensing image registration", Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74941B (30 October 2009); https://doi.org/10.1117/12.832498

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