Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (8): 134-138.doi: 10.11896/jsjkx.200600140

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Study on Aerial Image Fast Registration from UAV

HU Yu-cheng, RUI Ting, YANG Cheng-song, WANG Dong, LIU Xun   

  1. College of Field Engineering,Army Engineering University of PLA,Nanjing 210007,China
  • Received:2020-06-22 Revised:2020-09-27 Published:2021-08-10
  • About author:HU Yu-cheng,born in 1995,postgra-duate.His main research interests include image processing,pattern recognition and artificial intelligence.(361721310@qq.com)RUI Ting,born in 1972,Ph.D,professor,Master's advisor,is a member of China Computer Federation.His main research interests include image processing,pattern recognition and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China(61671470) and National Key R&D Program of China(2016YFC0802904).

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Abstract: In order to improve the real time of the UAV aerial image registration,the paper analyzes the relative stability of UAV's altitude and the lack of high-frequency details in the image,proposes an improved SIFT feature point extraction algorithm and constructs a special aerial images dataset for image mosaic for experimental verification.The paper first analyzes the theoretical basis and implementation method of scale invariance of SIFT (Scale Invariant Feature Transform),and puts forward eliminating redundant performance.The measures,such as reduction of Octave and Level of Gauss pyramid,and selecting the third Level image in each Octave to detect extreme points are taken to reduce the scale of differential scale space.Lastly,the comparable experiments based on dataset with state-of-art image mosaic methods are conducted.The experimental results show that the method proposed in this paper can extract robust feature points,and the matching time is only 1/10 of the original sift,which provides technical support for real-time image mosaic of UAV.

Key words: Aerial image, Differential scale space, Image registration, Scale invariance, SIFT

CLC Number: 

  • TP391.41
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