顾及高程平面及视差约束的最小二乘影像匹配算法

doi:10.3969/j.issn.0255-8297.2018.05.010

应用科学学报 ›› 2018, Vol. 36 ›› Issue (5): 826-836.doi: 10.3969/j.issn.0255-8297.2018.05.010

顾及高程平面及视差约束的最小二乘影像匹配算法

张春森¹, 牟岩¹, 朱师欢¹, 郭丙轩², 仇振国¹

1. 西安科技大学测绘科学与技术学院, 西安 710054;
2. 武汉大学测绘遥感信息工程国家重点实验室, 武汉 430079

收稿日期:2017-09-18 修回日期:2017-11-21 出版日期:2018-09-30 发布日期:2018-09-30
作者简介:张春森,教授,研究方向:摄影测量与遥感,E-mail:zhchunsen@aliyun.com
基金资助:
国家重点研发计划（No.2016YFB0502100）资助

Least Square Image Matching Algorithm Considering Elevation Plane and Parallax Constraints

ZHANG Chun-sen¹, MU Yan¹, ZHU Shi-huan¹, GUO Bing-xuan², QIU Zhen-guo¹

1. College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China;
2. State Key Laboratory of Information Engineering in Surveying, Mapping & Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2017-09-18 Revised:2017-11-21 Online:2018-09-30 Published:2018-09-30

摘要/Abstract

摘要： 针对传统最小二乘影像匹配受影像质量和初值条件限制的问题，结合铅垂线轨迹法（vertical line locus，VLL）与基于共线条件约束的多片最小二乘匹配算法（multiphoto geometrically constrained matching，MPGC）的特点，提出了一种顾及高程平面及视差约束的最小二乘影像匹配算法.该算法基于高程平面约束匹配，在匹配过程中顾及视差约束进行搜索匹配.视差偏移量可为最小二乘匹配提供可靠稳定的初值条件，能够实现影像的高精度匹配.为验证算法的有效性，分别采用铅垂线轨迹法、基于共线条件约束的多片最小二乘匹配算法与该算法进行对比分析实验.结果表明，该算法在匹配准确性以及后续空中三角测量解算方面比上述两种算法更有优势.

关键词: 高程平面, 视差约束, 最小二乘匹配, 高精度匹配

Abstract: Aiming at the problem that the traditional least square image matching algorithm is limited by the image quality and the initial condition, this paper proposes a least square image matching algorithm considering elevation plane and parallax constraints by means of combining the characteristics of the vertical line locus and the multi-slice least square matching algorithm based on collinear constraints. The algorithm is based on elevation plane constraint matching where the parallax constraint is used in searching and matching process. The parallax offset can provide reliable and stable initial condition for the least square matching, realizing precisely matching of images. There experiments are conducted with the vertical line locus method, the multi-slice least square matching algorithm with collinear conditional constraints and with the proposed algorithm, respectively.Experimental results proof that the proposed algorithm has obvious advantages in matching accuracy and subsequent calculation of aerial triangulation, compard with the other two algorithms.

Key words: elevation plane, parallax constraint, least square match, high precision match

中图分类号:

张春森, 牟岩, 朱师欢, 郭丙轩, 仇振国. 顾及高程平面及视差约束的最小二乘影像匹配算法[J]. 应用科学学报, 2018, 36(5): 826-836.

ZHANG Chun-sen, MU Yan, ZHU Shi-huan, GUO Bing-xuan, QIU Zhen-guo. Least Square Image Matching Algorithm Considering Elevation Plane and Parallax Constraints[J]. Journal of Applied Sciences, 2018, 36(5): 826-836.

参考文献

[1] 李德仁,李明. 无人机遥感系统的研究进展与应用前景[J]. 武汉大学学报(信息科学版),2014, 39(5):505-513. Li D R, Li M. Research advance and application prospect of unmanned aerial vehicle remote sensing system[J]. Geomatics and Information Science of Wuhan University, 2014, 39(5):505-513. (in Chinese)
[2] 袁修孝,高宇,邹小容. GPS辅助空中三角测量在低空航测大比例尺地形测图中的应用[J]. 武汉大学学报(信息科学版),2012, 37(11):1289-1293. Yuan X X, Gao Y, Zou X R. Application of GPS-supported aerotriangulation in large scale topographic mapping based on low-altitude photogrammetry[J]. Geomatics and Information Science of Wuhan University, 2012, 37(11):1289-1293. (in Chinese)
[3] 张永生. 现场直播式地理空间信息服务的构思与体系[J]. 测绘学报,2011, 40(1):1-4. Zhang Y S. The conception and architecture of live-service for geospatial information[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(1):1-4. (in Chinese)
[4] 毕凯,李英成,丁晓波,刘飞. 轻小型无人机航摄技术现状及发展趋势[J]. 测绘通报,2015(3):27-31. Bi K, Li Y C, Ding X B, Liu F. Aerial photogrammetric technology of light small UAV:status and trend of development[J]. Bulletin of Surveying and Mapping, 2015(3):27-31. (in Chinese)
[5] 李德仁,肖雄武,郭丙轩. 倾斜影像自动空三及其在城市真三维模型重建中的应用[J]. 武汉大学学报(信息科学版),2016, 41(6):711-721. Li D R, Xiao X W, Guo B X. Oblique image based automatic aerotriangulation and its application in 3D city model reconstruction[J]. Geomatics and Information Science of Wuhan University, 2016, 41(6):711-721. (in Chinese)
[6] 杨爱玲,于洪伟,郑灿辉. 关于轻型无人机航摄影像的质量探讨[J]. 测绘与空间地理信息,2011, 34(2):185-187. Yang A L, Yu H W, Zheng C H. Quality discussion on the aerial image of light UAV[J]. Geomatics & Spatial Information Technology, 2011, 34(2):185-187. (in Chinese)
[7] 张剑清,潘励,王树根. 摄影测量学:2版[M]. 武汉:武汉大学出版社,2009.
[8] 范大昭,纪松,张永生. 物方多视匹配算法的严密投影辐射线模型[J]. 测绘科学,2014, 39(9):7-10. Fan D Z, Ji S, Zhang Y S. Research on rigorous projection line of object space multi-view matching model[J]. Science of Surveying and Mapping, 2014, 39(9):7-10. (in Chinese)
[9] 董莉,李浩,佟光成. VLL影像匹配的线路地形断面测绘方法[J]. 测绘科学,2013, 38(4):101-103. Dong L, Li H, Tong G C. Route section survey method based on VLL image matching[J]. Science of Surveying and Mapping, 2013, 38(4):101-103. (in Chinese)
[10] 齐润冰,宋伟东. 物方多视影像密集匹配方法[J]. 测绘科学,2015, 40(10):98-101. Qi R B, Song W D. A dense matching algorithm of multi-view image based on the object space[J]. Science of Surveying and Mapping, 2015, 40(10):98-101. (in Chinese)
[11] 杨楠,邵振峰,郭丙轩. 非固定初始面元的无人机影像点云优化算法[J]. 武汉大学学报(信息科学版),2016, 41(8):1013-1020. Yang N, Shao Z F, Guo B X. Point cloud optimization for UAV image based on non-fixed initial patch[J]. Geomatics and Information Science of Wuhan University, 2016, 41(8):1013-1020. (in Chinese)
[12] Baltsavias E P. Multiphoto geometrically constrained matching[J]. Mitteilungen, 1991:49.
[13] Rosenholm D. Multi-point matching using the least squares technique for evaluation of threedimensionl models[J]. Photogrammetric Engineering and Remote Sensing, 1987, 53(6):621-626.
[14] 陈晓辉, 马丽霞, 王晓光. 基于像方特征点的多视影像最小二乘匹配[J]. 测绘与空间地理信息, 2013, 36(10):248-254. Chen X H, Ma L X, Wang X G. The least-square matching of multi-view images based on photographical feature points[J]. Geomatics & Spatial Information Technology, 2013, 36(10):248-254. (in Chinese)
[15] 王竞雪,朱庆,王伟玺. 多匹配基元集成的多视影像密集匹配方法[J]. 测绘学报,2013, 42(5):691-698. Wang J X, Zhu Q, Wang W X. A dense matching algorithm of multi-view image based on the integrated multiple matching primitives[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(5):691-698. (in Chinese)
[16] Helava. Object space least-squares correlation[J]. Photogrammetric Engineering and Remote Sensing, 1988, 54(6):711-714.
[17] 张祖勋,张剑清. 数字摄影测量学[M]. 武汉:武汉大学出版社,2002.
[18] Harris C G, Stephens M J. A combined corner and edge detector[C]//Processing Fourth Alley Vision Conference, Manchester, 1988:147-151.
[19] Lowe D G. Object recognition from local scale-invariant features[C]//Proceeding of the International Conference on Computer Vision, 1999:1150-1157.
[20] Lowe D G. Distinctive image features from scale-invariant key points[J]. International Journal of Computer Vision, 2004, 60(2):91-110.

顾及高程平面及视差约束的最小二乘影像匹配算法

Least Square Image Matching Algorithm Considering Elevation Plane and Parallax Constraints

PDF

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]	张春森, 张会, 郭丙轩, 杜神斌, 张月莹. 顾及相机响应函数的无人机影像匀光匀色[J]. 应用科学学报, 2019, 37(6): 783-794.
[2]	杨惠雯, 方俊永, 赵冬. 基于改进分离阈值法的农作物遥感精细分类特征选择[J]. 应用科学学报, 2019, 37(1): 51-63.
[3]	马飞虎, 姜珊珊, 孙翠羽. PSInSAR在铅山县矿区地表沉降监测中的应用[J]. 应用科学学报, 2018, 36(6): 969-977.
[4]	莫悠, 钟若飞, 张振鑫. 移动与定点扫描结合的室内点云数据获取方法[J]. 应用科学学报, 2018, 36(5): 756-764.
[5]	巫兆聪, 项伟, 李俊, 杨志. 基于满足度评估的遥感应用需求聚类算法[J]. 应用科学学报, 2018, 36(4): 635-643.
[6]	彭宝江, 钟若飞, 孙海丽, 耿雨馨. 面向DEM构建的点云四叉树和R树混合索引研究[J]. 应用科学学报, 2018, 36(4): 644-654.
[7]	张莎莎, 王树根. 数字正射影像镶嵌的质量评价方法[J]. 应用科学学报, 2018, 36(3): 461-470.
[8]	巫兆聪, 刘培, 巫远. 一种多光谱遥感应用需求综合方法[J]. 应用科学学报, 2017, 35(5): 658-666.
[9]	刘亚文, 覃苏舜. 点云数据稀疏区域建筑物立面重建方法[J]. 应用科学学报, 2017, 35(2): 217-225.
[10]	黄俊杰, 谭波, 陈孝明, 陈江平, 阮羚, 冯莞舒, 熊宇. 用Apriori关联规则挖掘算法发现湖北电网雷击灾害的时空分布规律[J]. 应用科学学报, 2017, 35(1): 31-41.
[11]	刘海涛, 田雪虹, 王贵. 高性能欠驱动水面机器人的有限时间跟踪控制[J]. 应用科学学报, 2016, 34(2): 203-214.
[12]	樊敬敬, 张华, 郝明. 面向对象的机载LiDAR数据建筑物提取[J]. 应用科学学报, 2016, 34(1): 84-94.
[13]	李爱霞, 管海燕, 钟良, 于永涛. 基于张量投票的道路表面裂缝检测[J]. 应用科学学报, 2015, 33(5): 541-549.
[14]	孙振兴1,2,3，钟若飞1,2,3. 一种用于全景影像的测量方案[J]. 应用科学学报, 2015, 33(4): 399-406.
[15]	巫兆聪，徐卓知，杨帆. 遥感卫星应用需求满足度的模糊评估[J]. 应用科学学报, 2015, 33(3): 299-308.