Fully affine SAR image registration method based on feature points

In the fully affine synthetic aperture radar (SAR) image registration conditions, the scale change between reference images and registering images is non-isotropy, which makes it difficult to extract enough matching feature points for the traditional image registration method based on feature points. To deal with this problem, a new image registration algorithm based on feature points is proposed. The affine matrix is first decomposed into products of image rotation matrixes, scale change matrixes, and constant matrixes. Then the unknown parameters in scale change matrixes are estimated by the particle swarm optimization (PSO) method. Based on the estimation result, reference and registering images are normalized to suppress the non-isotropy scale change between them. After that, the scale invariant feature transform (SIFT) operator is employed to extract matching feature points, and the image registration is based on it. The experimental results show that, for the fully affine SAR image registration, the proposed algorithm can obtain more matching feature points than the existed methods, so it has a better performance.