An improved SIFT algorithm for registration between SAR and optical images

Abstract

Aiming at improving the performance of scale invariant feature transform (SIFT) algorithm during the registration of optical and synthetic aperture radar (SAR) images, a new SIFT algorithm is proposed. Firstly, the nonlinear diffusion scale space of optical and SAR images is constructed by using nonlinear diffusion filtering, the uniform gradient information is calculated by using multi-scale Sobel operator and multi-scale exponential weighted mean ratio operator respectively. Then, after removing the first layer of the scale space with the image blocking strategy, the scale space is partitioned, and Harris feature points are extracted on the basis of consistent gradient information to obtain stable and uniform point features. Descriptors are constructed based on gradient position and direction histogram templates and normalized to overcome nonlinear radiation differences between images. Finally, the correct matching point pairs are obtained by using bilateral fast approximate nearest neighbor (FLANN) search matching method and random sampling consistent (RANSAC) method, and the affine transformation model parameters are obtained. Compared with the other two algorithms, the CMR of this algorithm is improved by 80.53%, 75.61% and 81.74% respectively in the three groups of images, and the RMSE is reduced by 0.6491, 1.0287 and 0.6306 respectively.