S-AKAZE: An effective point-based method for image matching

This paper presents a new point-based matching method, which integrates A-KAZE feature with improved SIFT descriptor. In previous studies, all the SIFT-based algorithms use the Gaussian scale space and Gaussian derivatives as smoothing kernel, but the Gaussian blurring does not self-adapt to the natural boundaries of objects and smoothes details and noise to the same extent at all scale levels, which will reduce localization accuracy and distinctiveness. Unlike SIFT feature, A-KAZE feature is built on nonlinear scale space by using Fast Explicit Diffusion (FED) schemes, which can blur the noise and remain the details or edges at the same time. Therefore we replace SIFT with A-KAZE to conduct feature detection. Then, in order to solve the problem that the combination of A-KAZE feature and SIFT descriptor is not rotation invariant, we use a SURF-like method to calculate the dominant orientations of keypoints and hereafter our method is thus named as S-AKAZE. Experiments on Mikolajczyk and Schmid dataset prove the high accuracy of our proposed method and experiments on four different types of remote sensing image pairs demonstrate an outstanding performance in remote sensing image matching.