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词汇树索引约束的无人机影像快速特征匹配算法

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高效匹配对选择和影像特征匹配直接决定无人机影像运动恢复结构(structure from motion,SfM)的效率.联合词汇树的倒排和正向索引结构,提出无人机影像匹配对选择和特征匹配加速算法.首先,利用词汇树的单词-影像倒排索引结构,基于影像相似性的空间分布特征,设计了自适应阈值的词汇树检索算法;然后,利用词汇树的影像-单词正向索引结构,将显式最近邻描述子搜索简化为词汇树单词映射,设计了词汇树引导匹配算法.4组无人机影像的试验结果表明,所提的自适应阈值词汇树检索算法能够有效地筛选匹配对,避免传统固定阈值或比例算法导致的匹配对数量过多或不足的问题;在保证匹配精度的前提下,词汇树引导匹配算法的加速比达到156~228,并在无人机影像SfM空三中取得与经典最近邻搜索匹配相当的精度.
Fast Feature Matching of UAV Images via Indexing Constraints of Vocabulary Trees
Objectives:Efficient match pair selection and image feature matching directly affect the efficien-cy of structure from motion(SfM)-based 3D reconstruction for unmanned aerial vehicle(UAV)images.This paper combines the inverted and direct index structure of the vocabulary tree to achieve the speedup of match pair selection and feature matching for UAV images.Methods:First,for match pair selection,vo-cabulary tree-based image retrieval has been the commonly used technique.However,it depends on the fixed number or fixed ratio threshold for match pair selection,which may cause many redundant match pairs.An adaptive vocabulary tree-based retrieval algorithm is designed for match pair selection by using the word-image index structure and the spatial distribution of similarity scores,and it can avoid the draw-back of depending on fixed thresholds.Second,for feature matching,the nearest neighbor searching method attempts to compute the Euclidean distance exhaustively between two sets of feature descriptors,which causes high computational costs and generates high outlier ratios.Thus,a guided feature matching(GFM)algorithm is presented which casts the explicit closest descriptor searching as the direct assignment by using the image-word index structure of the vocabulary tree.Combining the match pair selection and GFM algo-rithm,an integrated workflow is finally presented to achieve feature matching of both ordered and unor-dered UAV images with high precision and efficiency.Results:The proposed workflow is verified using four UAV datasets and compared comprehensively with classical nearest neighbor searching algorithms and commercial software packages.Conclusions:The experimental results verify that the proposed method can achieve efficient match pair selection and avoid the problem of retrieving too many or too few match pairs that are usually caused by traditional methods using fixed threshold or number strategies.Without the sacri-fice of matching precision,the speedup ratio of direct assign based feature matching ranges from 156 to 228,and competitive accuracy is also obtained from 3D reconstruction compared with the nearest neighbor searching method.

unmanned aerial vehicle(UAV)match pair selectionfeature matchingvocabulary tree-based retrievalstructure from motion(SfM)3D reconstruction

姜三、江万寿、郭丙轩

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中国地质大学(武汉)计算机学院,湖北 武汉,430074

人工智能与数字经济广东省实验室(深圳),广东 深圳,518060

自然资源信息管理与数字孪生工程软件教育部工程研究中心,湖北 武汉,430074

武汉大学测绘遥感信息工程国家重点实验室,湖北 武汉,430079

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无人机 影像匹配对选择 特征匹配 词汇树检索 运动恢复结构 三维重建

2024

武汉大学学报(信息科学版)
武汉大学

武汉大学学报(信息科学版)

CSTPCD北大核心
影响因子:1.072
ISSN:1671-8860
年,卷(期):2024.49(9)