改进SIFT算法结合两级特征匹配的无人机图像匹配算法  

UAV Image Matching Algorithm Based on Improved SIFT Algorithm and Two-stage Feature Matching

作　　者：邵进达 杨帅 程琳[2] SHAO Jin-da;YANG Shuai;CHENG Lin(College of Geomatics Science and Technology,Nanjing Tech University,Nanjing 210000,China;School of Transportation,Southeast University,Nanjing 210096,China)

机构地区：[1]南京工业大学测绘科学与技术学院,南京210000 [2]东南大学交通学院,南京210096

出　　处：《计算机科学》2019年第6期316-321,共6页Computer Science

基　　金：国家自然科学基金项目(51378119)资助

摘　　要：针对无人机航拍图像匹配过程中所需时间长、成本高、计算量大的问题,提出一种几何代数法(Geometry Algebra,GA)和尺度不变特征变换(Scale Invariant Feature Transform,SIFT)结合的无人机图像匹配算法,以实现图像的快速特征提取和特征匹配。首先利用GA算法和SIFT算法进行特征点的检测及描述;接下来进行两级特征匹配,即先使用快速最近邻搜索包(Fast Library for Approximate Nearest Neighbors,FLANN)算法对特征点进行粗匹配,再根据改进的随机抽样一致算法(Random Sample Consensus,RANSAC)来优化匹配结果。实验结果表明,与传统的图像匹配方法相比,提出的算法可以准确地定位更多的特征点,极大地提高了图像对准过程的速度,并且可以为大型无人机图像匹配节省大量时间。Aiming at the problems in the matching process of UAV aerial images,such as long time,high cost and large amount of computation,this paper proposed an UAV image matching algorithm based on scale invariant feature transform scale invariant feature transform(SIFT)algorithm and geometrical algebraic method geometry algebra(GA)to achieve fast image feature extraction and feature matching.Firstly,the feature points are detected and described by using GA method and SIFT algorithms.Then,two-level feature matching is performed,fast library for approximate nearest neighbors(FLANN)algorithm is used to pre-matching the feature points and matching results is optimized according to the improved random sample consensus(RANSAC)algorithm.Experimental results show that compared with traditio-nal image matching algorithm,the proposed algorithm can locate more feature points accurately and improve the speed of image alignment process greatly,and it can save a lot of time for image matching of large drones.

关 键 词：无人机图像匹配 几何代数法 SIFT算法 FLANN粗匹配 改进RANSAC算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]