无人机与卫星影像匹配的相似性逼近算法

doi:10.13474/j.cnki.11-2246.2023.0266

测绘通报 ›› 2023, Vol. 0 ›› Issue (9): 64-69.doi: 10.13474/j.cnki.11-2246.2023.0266

无人机与卫星影像匹配的相似性逼近算法

罗卿莉¹, 张书缤¹, 陈相¹, 魏钜杰²

1. 天津大学精密测试技术及仪器国家重点实验室, 天津 300072;
2. 中国测绘科学研究院, 北京 100036

收稿日期:2022-10-24 发布日期:2023-10-08
作者简介:罗卿莉(1985—),女,博士,副教授,研究方向为摄影测量与遥感。E-mail:luoqingli@tju.edu.cn
基金资助:
城市轨道交通数字化建设与测评技术国家工程实验室开放课题基金(2021ZH04);天津市自然科学基金重点项目(21JCZDJC00670);天津市交通运输科技发展计划(2022-40;2020-02);国家自然科学基金(41601446;41801284)

A similarity approximation algorithm for matching UAV and satellite image

LUO Qingli¹, ZHANG Shubin¹, CHEN Xiang¹, WEI Jujie²

1. State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China;
2. Chinese Academy of Surveying & Mapping, Beijing 100036, China

Received:2022-10-24 Published:2023-10-08

摘要/Abstract

摘要： 针对现有算法在无人机与卫星影像具有较大空间分辨率差异时存在匹配正确率不高的问题,本文提出了一种无人机与卫星影像匹配的相似性逼近算法。该方法在匹配前对无人机影像进行高斯滤波处理,提高了无人机与卫星影像的尺度相似性,并最终提高了匹配算法的正确率。试验结果表明,该算法具有较高的匹配精度,适用于无人机与卫星影像空间分辨率差异较大时无人机影像的匹配与自主定位。

关键词: 无人机, 自主定位, 相似性逼近, 影像匹配

Abstract: The matching accuracy of the current algorithms is not high enough when UAV and satellite images have a large spatial resolution difference. In order to avoid it, a similarity approximation algorithm for matching UAV and satellite images is proposed. The algorithm applies Gaussian filtering to the UAV images before matching, improves the scale similarity between the UAV image and the satellite image, and finally improves the accuracy of the matching algorithm. Experimental results demonstrate it has high matching accuracy and it is suitable for UAV image matching and autonomous positioning when the spatial resolution difference between UAV image and satellite image is large.

Key words: UAV, autonomous positioning, similarity approximation, image matching

中图分类号:

P237

罗卿莉, 张书缤, 陈相, 魏钜杰. 无人机与卫星影像匹配的相似性逼近算法[J]. 测绘通报, 2023, 0(9): 64-69.

LUO Qingli, ZHANG Shubin, CHEN Xiang, WEI Jujie. A similarity approximation algorithm for matching UAV and satellite image[J]. Bulletin of Surveying and Mapping, 2023, 0(9): 64-69.

参考文献

[1] 贾迪, 朱宁丹, 杨宁华, 等. 图像匹配方法研究综述[J]. 中国图象图形学报, 2019, 24(5):677-699.
[2] BROWN L G. A survey of image registration techniques[J]. ACM Computing Surveys (CSUR), 1992, 24(4):325-376.
[3] ZITOVÁ B,FLUSSER J. Image registration methods:a survey[J]. Image and Vision Computing,2003,21(11):977-1000.
[4] XU Yichun,LIU Yong,SHI Qian. Image feature point matching method based on improved BRISK[J]. International Journal of Wireless and Mobile Computing,2021,20(2):132.
[5] ZHONG Bing,LI Yubai. Image feature point matching based on improved SIFT algorithm[C]//Proceedings of the 4th International Conference on Image,Vision and Computing (ICIVC). Xiamen:IEEE,2020:489-493.
[6] WANG Jingxue,ZHU Qing,LIU Suyan,et al. Robust line feature matching based on pair-wise geometric constraints and matching redundancy[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2021,172:41-58.
[7] GUANG Xingxing,GAO Yanbin,LIU Pan,et al. A novel line feature description and matching method for visual-aided inertial navigation system[J]. Journal of Physics:Conference Series,2019,1267(1):012046.
[8] WANG Yongchao,YUAN Yijun,LEI Zhao. Fast SIFT feature matching algorithm based on geometric transformation[J]. IEEE Access,2020,8:88133-88140.
[9] DENG Zhengyan,WANG Yuzeng,ZHANG Haoran. Research on feature extraction of veneer defects based on improved SURF algorithm[J]. Journal of Physics:Conference Series,2020,1650(3):032180.
[10] CAI Lecai,YE Yuling,GAO Xiang,et al. An improved visual SLAM based on affine transformation for ORB feature extraction[J]. International Journal for Light and Electron Optics,2021,227:165421.
[11] MOREL J M,YU Guoshen. ASIFT:a new framework for fully affine invariant image comparison[J]. SIAM Journal on Imaging Sciences,2009,2(2):438-469.
[12] 王子豪. 基于图像匹配的目标定位关键技术研究[D]. 成都:电子科技大学,2020.
[13] CHEN M,ZHU Q,YAN S,et al. Lgs:local geometrical structure-based interest point matching for wide-baseline imagery in urban areas[J]. ISPRS Annals of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2019,IV-2/W5:13-20.
[14] ZHOU Lei,ZHU Siyu,SHEN Tianwei,et al. Progressive large scale-invariant image matching in scale space[C]//Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). Venice:IEEE,2017:2381-2390.
[15] 敖天慈. 基于改进SURF算法的图像匹配[D]. 大连:大连理工大学,2019.
[16] CHEN Shichu,WANG Zhiqiang,REN Yan. A fast matching algorithm for the images with large scale disparity[J]. Mathematical Foundations of Computing,2020,3(3):141-155.

无人机与卫星影像匹配的相似性逼近算法

A similarity approximation algorithm for matching UAV and satellite image

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	麻德明, 张秀林, 田梓文, 王佳新. 无人机遥感影像浒苔信息提取方法[J]. 测绘通报, 2023, 0(8): 24-28.
[2]	王宇昊, 李登华, 丁勇. 无人机倾斜摄影测量技术支持下的裸露边坡表面变化识别[J]. 测绘通报, 2023, 0(8): 45-50.
[3]	张君华, 李娜, 张勤辉, 马小伟. 北移亚洲象群夜间位置监测方法[J]. 测绘通报, 2023, 0(8): 63-66.
[4]	刘冬枝, 许庆领. 高分七号与资源三号卫星联合服务机场净空监测[J]. 测绘通报, 2023, 0(6): 138-141.
[5]	孟永东, 袁昌纬, 田斌, 蔡征龙, 张伟杰. 滑坡表面位移的无人机航测点云比对监测方法[J]. 测绘通报, 2023, 0(5): 1-8.
[6]	何雨薇, 诸葛盛, 徐祥鹏, 钟立军, 杨夏, 张小虎. 桥梁自然特征点的快速精确跟踪算法[J]. 测绘通报, 2023, 0(5): 101-106,163.
[7]	张适然, 陈明辉, 黄燕. 基于实景三维的东莞自建住宅提取与特征分析[J]. 测绘通报, 2023, 0(5): 120-124,163.
[8]	董志洋, 李慧, 葛靖宇, 程建华. 改进蚁群算法的无人机三维环境路径规划[J]. 测绘通报, 2023, 0(5): 153-157.
[9]	田正杰, 王雪英, 渠涛, 李泽晖. 无人机载LiDAR在光伏场区1∶500地形图测绘中的应用[J]. 测绘通报, 2023, 0(5): 180-184.
[10]	陈宗杰, 杨武年, 张琬琳, 刘明鑫, 董秀军, 何先定. 基于植被冠层含水量反演数据的森林易燃程度评价[J]. 测绘通报, 2023, 0(4): 99-105.
[11]	全昌文, 李正洪, 庞百宁, 熊毅飞. 无人机影像密集匹配点云在违法占地和违法建筑监测中的应用[J]. 测绘通报, 2023, 0(4): 111-114,134.
[12]	张林杰, 黄筱, 饶维冬, 罗卫, 左强, 熊豪, 黎文辉. 网络RTK和PPK辅助水利工程免像控无人机倾斜摄影测量三维建模分析[J]. 测绘通报, 2023, 0(4): 115-120.
[13]	张永彬, 刘雅辉, 刘明月, 满卫东, 宋唐雷, 李春雨. 基于Relief F与卷积神经网络的湿地植物群落精细分类[J]. 测绘通报, 2023, 0(2): 58-64.
[14]	宗慧琳, 袁希平, 甘淑, 张晓伦, 梁昌献, 赵振峰. 改进AKAZE算法的泥石流区无人机影像特征匹配[J]. 测绘通报, 2023, 0(2): 91-96,103.
[15]	朱晓武, 王斌, 刘金沧, 黄小川. 联合船载单波束潮间带机载LiDAR测量系统[J]. 测绘通报, 2023, 0(2): 145-149.