MULTI-SOURCE REMOTE SENSING IMAGES MATCHING BASED ON IMPROVED KAZE ALGORITHM

Abstract. SIFT as the representative of the same feature point extraction and matching algorithm has been widely applied in the field of multisource remote sensing image matching. However, it eliminates noise and detects features at different scale levels by building or approximating the Gaussian scale space based on linear. Gaussian blurring does not respect the natural boundaries of objects and smoothes to the same degree details and noise, reducing localization accuracy. To solve this problem, we proposed an improved KAZE algorithm which can build stable nonlinear scale space. Firstly, the extreme points are detected through building stable nonlinear scale space. Secondly, The match result by optimizing the feature points and strictly limiting matching threshold is used to calculate geometric transformation model parameters between two image. Finally, we can use this geometric transformation model to restrict the search space for feature points matching. Experimental results show that the improved KAZE algorithm is significantly better than the before KAZE. Moreover, for detail and texture blurred images, KAZE and its improved algorithm have unique advantages compared to the SIFT.