An enhanced multi-view vertical line locus matching algorithm of object space ground primitives based on positioning consistency for aerial and space images

被引:8
作者
Zhang, Ka [1 ,2 ,3 ]
Sheng, Yehua [1 ,2 ,3 ]
Wang, Meizhen [1 ,2 ,3 ]
Fu, Suxia [1 ,2 ]
机构
[1] Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, 1 WenYuan Rd, Nanjing 210023, Jiangsu, Peoples R China
[2] State Key Lab Cultivat Base Geog Environm Evolut, 1 WenYuan Rd, Nanjing 210023, Jiangsu, Peoples R China
[3] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, 1 WenYuan Rd, Nanjing 210023, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Multi-view image matching; Object space ground primitive; Positioning consistency; Vertical line locus; ENERGY MINIMIZATION; POINT CLOUDS; STEREO; REGISTRATION; ROBUST; LIDAR; RECONSTRUCTION; EXTRACTION; GENERATION; MODEL;
D O I
10.1016/j.isprsjprs.2018.03.017
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
The traditional multi-view vertical line locus (TMVLL) matching method is an object-space-based method that is commonly used to directly acquire spatial 3D coordinates of ground objects in photogrammetry. However, the TMVLL method can only obtain one elevation and lacks an accurate means of validating the matching results. In this paper, we propose an enhanced multi-view vertical line locus (EMVLL) matching algorithm based on positioning consistency for aerial or space images. The algorithm involves three components: confirming candidate pixels of the ground primitive in the base image, multi-view image matching based on the object space constraints for all candidate pixels, and validating the consistency of the object space coordinates with the multi-view matching result. The proposed algorithm was tested using actual aerial images and space images. Experimental results show that the EMVLL method successfully solves the problems associated with the TMVLL method, and has greater reliability, accuracy and computing efficiency. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:241 / 254
页数:14
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