Research On UAV Image Matching Algorithm Based On Improved AKAZE And K-VFC

With the rapid development of UAV photogrammetry platform and computer vision technology.UAV photogrammetry platforms are widely used in land and resources surveys,three-dimensional modeling,object recognition,target tracking,surveying and mapping and other fields due to their advantages such as portability,easy operation,low cost,high efficiency and quickness,and strong maneuverability.One of the focuses of many scholars'research.Due to the small size and light weight of the UAV,it is easily affected by harsh environments and wind during flight,and it is easy to generate image data such as rotation,viewing angle deviation,and translation,which has a great impact on the realization of fast and high-precision image matching results..Therefore,based on the principles and characteristics of the traditional feature matching algorithm,this paper improves it to achieve high-efficiency and high-precision matching of UAV images.The main research contents of this paper are as follows:1.Based on the feature matching algorithm,analyze the basic principles and matching process of the commonly used SIFT algorithm,SURF algorithm,ORB algorithm,KAZE algorithm,and AKAZE algorithm.Using the Mikolajczyk data set as the experimental data,perform feature matching experiments on the five algorithms.Compare and analyze the matching rate and matching accuracy rate,and compare the robustness of each algorithm.2.Aiming at the image matching based on the feature matching algorithm that uses the gray image as the input object,the color feature information of the UAV image is not fully utilized,so this paper uses the AKAZE algorithm to perform the image feature detection feature,and uses the color space information The Opponent-DAISY descriptor is used to describe features,using 3 sets of UAV images to change different variables(rotation,size,brightness,blur),and perform feature matching experiments on images with different variable changes to verify the robustness of the improved algorithm,And compare it with traditional algorithms.3.There is no limit to the number of iterations of the RANSAC algorithm for precise matching,and it is not necessarily the optimal result point to artificially set the limit.The VFC algorithm is introduced to accurately match UAV images,and the matching effect is poor in an environment with weak color contrast.In this paper,the K-VFC combined algorithm is used to eliminate mismatch points,so as to improve the accuracy of UAV image matching.Through experiments,it is found that the matching accuracy of the K-VFC algorithm can reach more than 80%,which is 15%higher than the accuracy of the RANSAC algorithm and the VFC algorithm,indicating that the K-VFC algorithm can effectively improve the effect of UAV image matching.