RESEARCH ON UAV IMAGE REGISTRATION BASED ON SIFT ALGORITHM ACCELERATION

被引:0
作者
Li, Wei [1 ]
Li, Changhui [1 ]
Wang, Feng [1 ]
机构
[1] Guangzhou Urban Planning & Design Survey Res Inst, Guangzhou 510060, Peoples R China
来源
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年
关键词
SIFT algorithm; UAV image; feature matching; Bidirectional Matching; FEATURES;
D O I
10.1109/igarss.2019.8900483
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Considering the low real-time performance and the large amount of false matches exist in the feature matching stage of traditional Scale Invariant Feature Transform(SIFT) algorithm in unmanned aerial vehicle (UAV) remote sensing image registration. In this paper, a series of optimization methods for traditional SIFT algorithms are proposed, including SIFT execution process optimization, changing the parameters of scale space construction, Simplified method for judging the construction area of feature descriptor and construction of bidirectional matching filters. Experiments on UAV remote sensing images show that the optimization method can significantly improve the matching efficiency compared with traditional methods, and the comprehensive acceleration ratio is about 35% to 40%, which proves the effectiveness of the acceleration method.
引用
收藏
页码:2447 / 2450
页数:4
相关论文
共 10 条
[1]   SIFT optimization and automation for matching images from multiple temporal sources [J].
Castillo-Carrion, Sebastian ;
Guerrero-Ginel, Jose-Emilio .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2017, 57 :113-122
[2]   Medium-low resolution multisource remote sensing image registration based on SIFT and robust regional mutual information [J].
Chen, Shuhan ;
Li, Xiaorun ;
Zhao, Liaoying ;
Yang, Han .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2018, 39 (10) :3215-3242
[3]  
Guo GD, 2003, LECT NOTES COMPUT SC, V2888, P986
[4]   AN ANALYSIS AND ALGORITHM FOR POLYGON CLIPPING [J].
LIANG, YD ;
BARSKY, BA .
COMMUNICATIONS OF THE ACM, 1983, 26 (11) :868-877
[5]   Distinctive image features from scale-invariant keypoints [J].
Lowe, DG .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2004, 60 (02) :91-110
[6]  
Luo J., 2009, International Journal of Image Processing, V3, P143
[7]   A performance evaluation of local descriptors [J].
Mikolajczyk, K ;
Schmid, C .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2005, 27 (10) :1615-1630
[8]  
Munich ME, 2006, IEEE ROBOT AUTOM MAG, V13, P72, DOI 10.1109/MRA.2006.1678141
[9]   USAC: A Universal Framework for Random Sample Consensus [J].
Raguram, Rahul ;
Chum, Ondrej ;
Pollefeys, Marc ;
Matas, Jiri ;
Frahm, Jan-Michael .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2013, 35 (08) :2022-2038
[10]  
Zhang H, 2018, INT C INT TRANSP