基于改进SIFT的无人机航拍图像快速配准研究  

Study on Aerial Image Fast Registration from UAV

作　　者：胡育诚 芮挺 杨成松 王东 刘恂 HU Yu-cheng;RUI Ting;YANG Cheng-song;WANG Dong;LIU Xun(College of Field Engineering,Army Engineering University of PLA,Nanjing 210007,China)

机构地区：[1]陆军工程大学野战工程学院,南京210007

出　　处：《计算机科学》2021年第8期134-138,共5页Computer Science

基　　金：国家自然科学基金(61671470);国家重点研发计划(2016YFC0802904)。

摘　　要：为了提高无人机航拍图像配准的实时性,通过分析无人机巡航高度相对稳定及图像缺乏高频的细节信息的特点,提出了一种改进SIFT特征点检测方法,显著提高了图像的配准速度,并构建了一个用于图像拼接的航空影像数据集进行实验验证。首先分析了SIFT(Scale Invariant Feature Transform)算法关于特征点尺度不变性的理论依据及实现方法,提出了消除冗余性能的策略;然后采用减少高斯金字塔阶数与层数以及选择在每阶的第三层图像开始检测极值点,以减小差分尺度空间规模的方法;最后在数据集上进行了与现有图像配准方法的对比实验。实验结果证明,所提方法能够获得匹配稳健、鲁棒性高的特征点,匹配耗时只有经典SIFT的1/10,该方法为无人机航拍图像快速拼接提供了技术支持。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.

关 键 词：航拍图像 图像配准 SIFT 差分尺度空间 尺度不变性 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]