联合对数极坐标描述与位置尺度特征的无人机影像匹配算法  

A UAV Image Matching Algorithm Considering log-Polar Description and Position Scale Distance Feature

作　　者：姚永祥 段平[1] 李佳[1] 王云川 YAO Yongxiang;DUAN Ping;LI Jia;WANG Yunchuan(Faculty of Geography,Yunnan Normal University,Kunming 650500,China;School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,China)

机构地区：[1]云南师范大学地理学部,云南昆明650500 [2]武汉大学遥感信息工程学院,湖北武汉430079

出　　处：《武汉大学学报（信息科学版）》2022年第8期1271-1278,共8页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金(41961061);云南省基础研究计划(202001AT070057);云南省教育厅科学研究基金(2018JS148)。

摘　　要：针对无人机(unmanned aerial vehicle,UAV)影像提取的同名点数量较少,从而影响影像间位姿信息的计算,导致影像拼接错位、平差解算不严密甚至失败等问题,提出了一种联合对数极坐标描述与位置尺度特征的匹配算法。首先,建立高斯多尺度影像集合进行特征点提取;其次,采用对数极坐标进行描述子构建,建立适合UAV影像特征的描述子;然后,通过位置和尺度约束的距离匹配函数进行特征匹配;最后,通过模式搜索和快速样本共识方法剔除粗差后完成同名点提取。将四旋翼UAV获取的影像作为实验数据,与SIFT(scale invariant feature transform)算法和SAR-SIFT(synthetic aperture radar-SIFT)算法进行了影像匹配的对比实验。结果表明,所提算法可以较好地提取UAV影像的同名点对。Objectives: Few corresponding points can easily affect the calculation of image pose information, increase the difficulty of constructing a regional network in an aerial triangulation solution, so that lead to problems such as image stitching misalignment, incorrect bundle adjustment results or even failure.In order to better complete the matching of unmanned aerial vehicle(UAV) images, this paper proposes a robust UAV image matching algorithm considering log-polar description and position scale distance.Methods: Firstly, a Gaussian multi-scale image collection is established and feature points are extracted.Secondly, the descriptors are constructed using log-polar coordinates, and a descriptor suitable for UAV image characteristics is established. Then, the feature matching is performed by the distance function of position and scale constraints. Finally, the mode seeking and fast sample consensus method are used to eliminate the outliner and complete the extraction of correspondence.Results: The image obtained by four-rotor UAV is used as the data source, and a comparison experiment of image matching with scale invariant feature transform(SIFT) algorithm and synthetic aperture radar-scale invariant feature transform(SARSIFT) algorithm is carried out. The experimental results show that a 210-dimensional log-polar coordinate descriptor is constructed through the gradient location and orientation histogram. The descriptor can better describe the feature points in 10 directions through the circular neighborhood, making the matching results more robust. The position scale Euclidean distance matching function established by integrating factors such as position and scale can better calculate the UAV image matching relationship, and match more correct corresponding points. In terms of the number of correct corresponding points extracted under the same parameter settings, the proposed algorithm is significantly more than the other two algorithms, and in terms of the root mean square error of the matching results, the alg

关 键 词：无人机影像 特征提取 对数极坐标描述子 位置尺度距离匹配 精度检验 

分 类 号：P237[天文地球—摄影测量与遥感]