多源遥感数据实景三维立体化重构技术与发展  

Technology and development for reconstruction of 3D realistic geospatial landscape model from multi-source remote sensing data

作　　者：刘欣怡 张永军[1,2] 岳冬冬 范伟伟 万一 李廷赟 钟佳辰 刘嘉豪 刘校安 LIU Xinyi;ZHANG Yongjun;YUE Dongdong;FAN Weiwei;WAN Yi;LI Tingyun;ZHONG Jiachen;LIU Jiahao;LIU Xiaoan(School of Remote Sensing Information Engineering,Wuhan University,Wuhan 430079,China;Technology Innovation Center for Collaborative Applications of Natural Resources Data in GBA,Ministry of Natural Resources,Guangzhou 510075,China)

机构地区：[1]武汉大学遥感信息工程学院,武汉430079 [2]自然资源部粤港澳大湾区自然资源数据协同应用工程技术创新中心,广州510075

出　　处：《时空信息学报》2025年第1期20-30,共11页JOURNAL OF SPATIO-TEMPORAL INFORMATION

基　　金：国家自然科学基金项目(42201474)。

摘　　要：实景三维立体化重构技术依托多源遥感数据的时空互补、多视协同等优势,通过融合多传感器观测数据获取高精度、多维度时空数据,为实景三维模型数据供给与应用提供基础,是实景三维中国数智化建设的主体技术之一。当前研究在异源数据智能配准、几何重建与语义理解等关键环节取得突破,但仍面临跨平台数据时空基准不统一、复杂场景自适应建模能力不足等挑战。本文系统梳理多源遥感数据驱动的实景三维模型立体化重构技术体系,重点介绍实景三维模型立体化重构的主要数据源与实现路径,深入剖析当前仍存在的瓶颈问题,并从生成式AI驱动建模、动态场景时序重建、多源数据协同利用、应用驱动产品衍生等方面讨论立体化重构技术的最新前沿与发展趋势。3D realistic geospatial landscape model(3DRGLM)stereoscopic reconstruction technology plays a pivotal role in China’s digital transformation by leveraging the spatiotemporal complementarity and multi-view synergy of multi-source remote sensing data to achieve high-precision,multi-dimensional virtual space modeling.This article systematically reviews the technical framework of multi-source remote sensing data-driven 3D realistic geospatial landscape model reconstruction,covering data sources,geographic scene and entity modeling methods,technical challenges,and emerging trends.Key data sources include optical imagery(satellite,aerial,and close-range),LiDAR point clouds(airborne,terrestrial,and mobile systems),and SAR data.Satellite optical imagery facilitates large-scale terrain monitoring,while aerial and close-range imagery improve urban and component-level modeling.LiDAR provides high-precision 3D spatial information,with mobile systems enhancing efficiency through colored point cloud acquisition.SAR data,when combined with InSAR-derived deformation point clouds,strengthens the reconstruction of complex terrain.Additionally,IoT-generated real-time data and historical geospatial data contribute to the dynamic maintenance of 3D models.Geographic scene modeling primarily relies on mesh generation using multi-view stereo(MVS)and 3D Gaussian splatting(3DGS).Traditional MVS methods encounter difficulties in feature matching and environmental adaptability,while deep learning frameworks optimize pixel-level geometry.Transformer-based models enable joint camera calibration and 3D reconstruction from unconstrained images.3DGS excels in visual fidelity and real-time rendering but faces challenges in maintaining multi-view geometric consistency.Large-scale reconstruction approaches balance detail preservation and computational efficiency through dynamic partitioning and distributed training,although cross-region fusion remains challenging.Geographic entity modeling integrates model-driven(template-based)and data-driven(pr

关 键 词：实景三维中国 立体化重构 多源遥感数据 模型重建 地理场景建模 地理实体建模 

分 类 号：P23[天文地球—摄影测量与遥感]