稀疏点云引导的航空影像数字表面模型生成方法  

Sparse Point Cloud Guided Digital Surface Model Generation for Aerial Images

作　　者：张永军[1] 邹思远 刘欣怡 ZHANG Yongjun;ZOU Siyuan;LIU Xinyi(School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,China)

机构地区：[1]武汉大学遥感信息工程学院,湖北武汉430079

出　　处：《武汉大学学报（信息科学版）》2023年第11期1854-1862,共9页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金(42201474);湖北省科技重大项目(2021AAA010-3);贵州省科技重大专项计划([2022]001)。

摘　　要：密集匹配是生成数字表面模型的核心步骤,但在纹理缺乏、视差断裂和光照不一致等区域容易匹配失败。为了提高密集匹配结果的精度,提出一种稀疏点云引导(sparse point cloud guidance,SPCG)的航空影像数字表面模型生成方法,旨在利用空三加密的稀疏点云约束影像的密集匹配。首先,通过稀疏点云引导的方式,选择具有良好几何配置、高重叠度和高覆盖率的立体影像对;然后,利用最近邻聚类和金字塔传播方法,扩充稀疏点云的数量;进一步,采用改进的高斯函数优化扩展点的匹配代价,以提高密集匹配结果的准确性;最后,将多个密集匹配点云融合,生成数字表面模型。模拟立体影像和真实航空立体影像的实验表明,SPCG方法优化的半全局匹配显著提升了原始半全局匹配算法的匹配准确性,具体数值表现如下:半全局匹配生成的视差图与真实视差的差值大于1、2或3个像素的百分比分别为46.72%、32.83%或27.32%,而SPCG方法优化的半全局匹配相比于半全局匹配分别下降了7.67%、9.75%或10.28%。此外,相比于高斯方法优化的半全局匹配和深度学习方法金字塔立体匹配网络,SPCG方法优化的半全局匹配具有最高的匹配精度。多视航空影像实验结果表明,SPCG方法准确生成了整个测区的数字表面模型,并且在定性和定量两个方面均优于采用卓越SURE软件生成的数字表面模型。Objectives:Digital surface model is of great significance in the fields of real-life 3D modeling,smart city construction,natural resources management,geoscience research,and hydrology and water resources management.However,dense matching,as a core step in generating digital surface models,is prone to matching failures in regions with a lack of texture,disparity gap and inconsistent illumination.The sparse point cloud data with high accuracy and extensive coverage after aerial triangulation,which can be used as a priori information to improve the accuracy of dense matching results.Methods:First,this paper proposes a sparse point cloud guidance(SPCG)method for generating digital surface models of aerial images.The method aims to constrain the dense matching of images using sparse point cloud encrypted by aerial triangulation.The sparse point cloud guidance first selects stereo image pairs with good geometric configurations,high overlap,and extensive coverage.Then,the number of sparse points is extended by using the closest proximity clustering and pyramid propagation methods.Additionally,the matching cost of the extended points is optimized by using the improved Gaussian function to enhance the accuracy of the dense matching results.Finally,the sparse point cloud is fused with the dense matching point cloud to generate the digital surface model.Results:Experiments on simulated stereo images and real aerial stereo images show that the optimized semi-global matching by the SPCG method in this paper significantly improves the matching accuracy of the original semi-global matching algorithm and outperforms the semi-global matching optimized by the Gaussian method and the deep learning method,pyramid stereo matching network.Numerically,the percentages of disparity maps generated by semi-global matching with greater than 1,2,or 3 pixels difference from the true disparities are 46.72%,32.83%,or 27.32%,respectively,whereas the SPCG method decreases by 7.67%,9.75%,or 10.28%,respectively,compared to the former.The experiment

关 键 词：航空影像 稀疏点云 半全局匹配 视差图 数字表面模型 

分 类 号：P237[天文地球—摄影测量与遥感]