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J. Hefei Univ. Tech. (Nat. Sci.)

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

Competent Authorities: Ministry of Education of the People's Republic of China

Sponsor by: Hefei University of Technology

ISSN 1000-1646, CN 34-1001/N

UAV image matching method based on improved SIFT and multiple constraints

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基于改进 SIFT 和多约束的 UAV 影像匹配方法

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Feb.2025, Vol.48 No.2: 212-219

DOI: 10.3969/j.issn.1003-5060.2025.02.010

Authors

HE Minglei, WANG Zhongyuan, QI Mingxin, YANG Zhenyu, YUAN Fang

(School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

Abstract and Keywords

Abstract: Aiming at the problems of instability of feature points and many mismatches in the process of unmanned aerial vehicle (UAV) image matching with scale invariant feature transform (SIFT) algorithm, a UAV image matching method based on improved SIFT and multiple constraints is proposed. Firstly, after the image downsampling, SIFT algorithm and Scharr-ORB algorithm are employed to detect and describe the feature points. Then, a rough matching of the feature points is established through multi-constraint methods of nearest neighbor distance ratio (NNDR), bidirectional constraint matching, and cosine similarity constraint matching. Finally, a multi-constraint method using least median of squares (LMedS) algorithm for fundamental matrix computation and random sample consensus (RANSAC) algorithm for homography matrix computation is adopted for the fine matching of feature points to further improve the matching accuracy. Experimental results show that this method can get more feature points and matching pairs while eliminating more mismatches, which makes the algorithm have higher correct matching rate (CMR) and matching accuracy.

Keywords: unmanned aerial vehicle (UAV) image; image matching; edge detection; multi-constraint method; fundamental matrix

作者信息

何明磊，王中元，戚铭心，杨振宇，袁芳

（中国矿业大学环境与测绘学院，江苏徐州221116）

摘要和关键词

摘要: 针对尺度不变特征转换(scale invariant feature transform, SIFT)算法在无人机(unmanned aerial vehicle, UAV)影像的匹配过程中存在特征点稳定性差和误匹配多的问题,文章提出一种基于改进SIFT和多约束的UAV影像匹配方法。首先,在对影像降采样后,综合采用SIFT算法和Scharr-ORB(oriented brief)算法共同进行特征点检测和描述;然后,使用最近邻距离比值法(nearest neighbor distance ratio, NNDR)、双向约束匹配和余弦相似度约束匹配的多约束方法进行特征点的粗匹配;最后,使用最小中值(least median of squares, LMedS)算法计算基础矩阵和随机抽样一致性(random sample consensus, RANSAC)算法计算单应矩阵的多约束方法进行特征点的精匹配,进一步提高匹配精度。结果表明,该方法在获得更多特征点和匹配对数的同时,能够剔除较多的误匹配,使其具有较高的匹配正确率和匹配精度。

关键词: 无人机（UAV）影像；影像匹配；边缘检测；多约束方法；基础矩阵

Funding

江苏省自然科学基金资助项目（BK20181361）

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