Quick stitching of unmanned aerial vehicle remote sensing images based on an improved SIFT algorithm
WANG Chao1， 2， LEI Tianjie3， ZHAGN Baoshan4， XU Ruirui3， CHEN Dongpan5
(1.Institute of Geographical Sciences of Henan Academy of Sciences， Zhengzhou 450052，China； 2.Collaborative Innovation Center of Geographic technology of Wisdom Central Plains， Zhengzhou 450052，China； 3.Institute of Environment and Sustainable Development in Agricultural， Chinese Academy of Agricultural Sciences， Beijing 100081，China； 4.Water Resources Bureau in Minquan County of Henan Province， Shangqiu 476800， Henan， China； 5.Beijing University of Technology， School of Artificial Intelligence and Automation， Beijing 100124，China)
Abstract：Unmanned aerial vehicle (UAV) remote sensing has been developing rapidly in the field of earth observation in recent decades. However， quick stitching of UAV images has become a problem of blocks for the wide applications. In this paper， an algorithm based on both random sample consensus (RANSAC) and least-squares match method was proposed to improve the image registration performance of SIFT algorithm. On the one hand， RANSAC was able to remove inaccurate feature point pairs that SIFT detected. On the other hand， given all rough feature matches based on SIFT features， least-squares match was used to carry out precise smatching. The experiment results show that our proposal was able to effectively estimate matching error with an average correct matching rate of 92.8%. Moreover， stitching accuracy was improved from 1.0 pixel to 0.1 pixel， and the stitching efficiency was also elevated. The improved SIFT perform fast and accurate matching in massive feature database， even in real time， and has stronger robustness， which would meet the demand for highly automated relative orientation of low-altitude remote sensing images with broad application prospects.
2023, Vol. 57 
