Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (3): 263-269.DOI: 10.3778/j.issn.1002-8331.2209-0151

• Graphics and Image Processing • Previous Articles     Next Articles

Research on UAV Image Stitching Technology Based on IB-SURF Algorithm

JIANG Zhi, JIANG Degang, HUANG Zijie, GUO Cailing, LI Bailin   

  1. 1.Graduate School of Tangshan, Southwest Jiaotong University, Tangshan, Hebei 063000, China
    2.Key Lab of Intelligent Equipment Digital Design and Process Simulation, Tangshan University, Tangshan, Hebei 063000, China
    3.School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610000, China
  • Online:2024-02-01 Published:2024-02-01



  1. 1.西南交通大学 唐山研究生院,河北 唐山 063000
    2.唐山学院 河北省智能装备数字化设计及过程仿真重点实验室,河北 唐山 063000
    3.西南交通大学 机械工程学院,成都 610000

Abstract: Aiming at the high computational complexity and low feature matching rate of the traditional SURF algorithm (speeded up robust features), when stitching multiple, high-resolution UAV images, the UAV images stitching technology based on the IB-SURF algorithm is proposed. Firstly, the overlapping area of the image is obtained by combining the POS (position and orientation system) information of the UAV. Then, a mask is constructed to detect feature points in the overlapping area of UAV images to reduce the time of feature extraction. Using the idea of Image Block (IB), the image is divided into grids, and the feature points are simply screened. Finally, neighborhood-K nearest neighbors (neighborhood-KNN) is introduced to match feature points to improve the efficiency of image matching. Experimental results show that IB-SURF algorithm has faster running speed and higher feature matching rate, the average feature matching rate reaches 84.3%, and the feature matching accuracy exceeds 95.1%. It provides a technical basis for high quality image stitching.

Key words: UAV images, image block-speeded up robust features (IB-SURF) algorithm, feature point extraction, image block

摘要: 针对传统SURF算法(speeded up robust features)在拼接高分辨率无人机航拍图像时运行速度慢、特征匹配率低的特点,提出了一种基于IB-SURF(image block-SURF)技术的无人机图像拼接算法。结合无人机定位定姿系统(position and orientation system,POS)求取图像重叠区域;构造掩模在无人机图像重叠区域检测特征点,减少特征提取时间;借助图像分块(image block,IB)的思想对图像划分网格,精简筛选特征点;引入Neighborhood-KNN (neighborhood-K nearest neighbors)进行特征点匹配,提高图像匹配效率。实验结果表明,IB-SURF算法有较快的运行速度和较高的特征匹配率,平均特征匹配率达到84.3%,特征匹配正确率超过95.1%,为图像高质量拼接提供了技术基础。

关键词: 无人机图像, IB-SURF算法, 特征点提取, 图像分块