测绘通报 ›› 2021, Vol. 0 ›› Issue (2): 36-39.doi: 10.13474/j.cnki.11-2246.2021.0039

• 学术研究 • 上一篇    下一篇

一种改进的无人机影像拼接粗差剔除算法

喜文飞1, 赵子龙2, 王绍君2, 沈志坚2, 李帅3   

  1. 1. 云南师范大学地理学部, 云南 昆明 650500;
    2. 云南海钜地理信息技术有限公司, 云南 昆明 650000;
    3. 96608部队, 河南 洛阳 471000
  • 收稿日期:2020-06-28 出版日期:2021-02-25 发布日期:2021-03-09
  • 作者简介:喜文飞(1984-),男,博士,讲师,主要研究方向为无人机图像处理、三维模型构建、变形监测。E-mail:xiwenfei911@163.com
  • 基金资助:
    云南省科技厅面上项目(202001AT070059)

An improved algorithm of UAV image mosaic gross error elimination

XI Wenfei1, ZHAO Zilong2, WANG Shaojun2, SHEN Zhijian2, LI Shuai3   

  1. 1. Faculty of Geography, Yunnan Normal University, Kunming 650500, China;
    2. Yunnan Haiju Geographic Information Technology Co., Ltd., Kunming 650000, China;
    3. 96608 Troops, Luoyang 471000, China
  • Received:2020-06-28 Online:2021-02-25 Published:2021-03-09

摘要: 利用无人机技术可以获取高分辨率影像。为了获取高精度的变换矩阵,提高影像匹配效率,本文对RANSAC算法进行了改进,加入影像的灰度信息进行约束,进一步剔除匹配粗差,最后采用均方根误差进行质量评判。为了验证算法的可靠性,选取一组山区影像和一组具有旋转偏角的建筑物影像进行验证。验证结果表明,匹配点粗差剔除率分别提高了15.15%和23.22%,本文算法的均方根误差较小,精度有显著的提高。

关键词: 无人机, 变换矩阵, 影像匹配, RANSAC算法, 灰度信息, 均方根误差

Abstract: High-resolution image can be obtained by UAV technology. In order to obtain the high-precision transformation matrix and improve the efficiency of image matching, this paper improves the RANSAC algorithm and adds the gray information of the image to constrain, which further eliminates the coarse error of matching. Finally, it uses the mean square root error to evaluate the quality. In order to verify the reliability of the algorithm, a group of mountain images and a group of building images with rotation angle are selected to verify. The results show that the rejection rate of matching points coarse error is increased by 15.15% and 23.22%, respectively. The mean square root error of the new algorithm is small and the accuracy is significantly improved.

Key words: UAV, transformation matrix, image matching, RANSAC algorithm, gray information, mean square root error

中图分类号: