Automatic generation DSM of UAV image based on random propagation COLVLL algorithm

被引：0
|
作者：
Zhang C. [1 ]
Ge Y. [1 ]
Guo B. [2 ]
Zhang Y. [1 ]
机构：
[1] College of Geomatics, Xi'an University of Science and Technology, Xi'an
[2] State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2022年 / 51卷 / 11期
基金：
中国国家自然科学基金;
关键词：
dense matching; DSM; object patch; UAV image; VLL algorithm;
D O I：
10.11947/j.AGCS.2022.20210325
中图分类号：
学科分类号：
摘要：
In view of the poor performance of existing dense matching methods in weak texture areas and areas with large height differences, and the loss of information when the dense matching results are fused to generate DSM, a DSM generation method based on random propagation COLVLL is proposed. Based on the effective image pair screening of the images after aerial triangulation photogrammetry, the random propagation mechanism is used to scan and iterate the DSM pixel area, combined with the VLL algorithm to iteratively update the randomly generated elevation value to obtain the DSM. Taking the UAV image with weak texture and large elevation difference as the experimental data, compare with the commercial software for generating DSM, and use the Vaihingen data set provided by ISPRS WGII/4 as a reference to test and analyze the DSM and real radiographic data generated by the method in this paper. The results show the effectiveness and applicability of the proposed method. © 2022 SinoMaps Press. All rights reserved.
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页码：2346 / 2354
页数：8
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