Automatic registration of Unmanned Aerial Vehicle remote sensing images based on an improved SIFT algorithm

Tianjie Lei; Lin Li; Guangyuan Kan; Zhongbo Zhang; Tao Sun; Xiaolei Zhang; Jianwei Ma; Shifeng Huang

doi:10.1117/12.2245126

29 August 2016 Automatic registration of Unmanned Aerial Vehicle remote sensing images based on an improved SIFT algorithm

Tianjie Lei, Lin Li, Guangyuan Kan, Zhongbo Zhang, Tao Sun, Xiaolei Zhang, Jianwei Ma, Shifeng Huang

Author Affiliations +

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100333R (2016) https://doi.org/10.1117/12.2245126
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

Unmanned Aerial Vehicle Remote Sensing (UAVRS) have developed rapidly driven mainly for military reconnaissance, earth observation and scientific data collection between military and civilian users over the past decade. However, automatic registration of UAVRS 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 method is proposed to improve the image registration performance of SIFT algorithm. On the one hand, RANSAC can remove inaccurate feature point pairs that SIFT detected. On the other hand, given all rough feature matches based on SIFT features, least-squares match is used to carry out precise matching. The experiment results show that our proposal can effectively estimate matching error with an average correct matching rate of 92.8%. And also the new algorithm had faster matching rate for the same number of images under the same experimental platform. As a result, the algorithm can improve greatly the accuracy of matching, but also to reduce the computation load based on the experiment results. Automatic registration of UAVRS images can be obtained in real time. After pre-matching by SIFT feature matching algorithm, the least squares matching is used to match accurately, which can be satisfied for the relative orientation of low-altitude remote sensing images automatically.

Citation Download Citation

Tianjie Lei, Lin Li, Guangyuan Kan, Zhongbo Zhang, Tao Sun, Xiaolei Zhang, Jianwei Ma, and Shifeng Huang "Automatic registration of Unmanned Aerial Vehicle remote sensing images based on an improved SIFT algorithm", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100333R (29 August 2016); https://doi.org/10.1117/12.2245126

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