无人机影像DSM自动生成随机传播COLVLL算法  

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

作　　者：张春森[1] 葛英伟 郭丙轩[2] 张月莹 ZHANG Chunsen;GE Yingwei;GUO Bingxuan;ZHANG Yueying(College of Geomatics,Xi'an University of Science and Technology,Xi'an 710054,China;State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China)

机构地区：[1]西安科技大学测绘科学与技术学院,陕西西安710054 [2]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079

出　　处：《测绘学报》2022年第11期2346-2354,共9页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(92038301)。

摘　　要：针对现有密集匹配方法在弱纹理及高差较大区域表现不佳,以及密集匹配融合生成DSM时导致信息丢失等问题,本文提出基于随机传播COLVLL的DSM生成方法。在对空三加密后的影像进行有效像对筛选的基础上,利用随机传播机制对DSM像素区域进行扫描迭代,结合VLL算法对随机生成的高程值进行迭代更新得到DSM。以弱纹理、大高差区域无人机影像为试验数据与现有生成DSM商业软件进行试验对比,并以ISPRS WGII/4提供的Vaihingen数据集为参考对本文方法生成DSM及真正射影像数据进行试验分析,结果均证明了本文方法的有效性和适用性。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.

关 键 词：数字表面模型 无人机影像 密集匹配 铅垂线轨迹法 物方面元 

分 类 号：P231[天文地球—摄影测量与遥感]