无人机SFM MVS摄影测量与遥感研究进展与挑战

E. F. Berra, M. Peppa
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引用次数: 18

摘要

在过去的十年里,对未命名飞行器(UAV)数据、运动结构(SfM)和多视点立体(MVS)摄影测量的兴趣得到了极大的扩展,彻底改变了航空遥感和测绘领域。本文综述了轻量化UA V的最新发展和应用,以及被广泛接受的SfM - MVS方法。首先,讨论了无人机遥感系统的优点和局限性,然后确定了应用于众多学科的不同UAV和小型化传感器模型,展示了最近使用的系统和传感器类型的范围。然后,简要介绍了SfM-MVS的优点和面临的挑战。总体而言,sfm - mvs衍生产品(例如正形图、数字表面模型)的准确性和质量取决于无人机数据集的质量、研究区域的特征和所使用的处理工具。为了更好地确定无人机SfM-MVS衍生输出的质量、精度和准确性,需要继续开发和研究。
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Advances and Challenges of UAV SFM MVS Photogrammetry and Remote Sensing: Short Review
Interest in Unnamed Aerial Vehicle (UAV)-sourced data and Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) photogrammetry has seen a dramatic expansion over the last decade, revolutionizing the fields of aerial remote sensing and mapping. This literature review provides a summary overview on the recent developments and applications of light-weight UA V s and on the widely-accepted SfM - MVS approach. Firstly, the advantages and limitations of UAV remote sensing systems are discussed, followed by an identification of the different UA V and miniaturised sensor models applied to numerous disciplines, showing the range of systems and sensor types utilised recently. Afterwards, a concise list of advantages and challenges of UAV SfM-MVS is provided and discussed. Overall, the accuracy and quality of the SfM-MVS-derived products (e.g. orthomosaics, digital surface model) depends on the quality of the UAV data set, characteristics of the study area and processing tools used. Continued development and investigation are necessary to better determine the quality, precision and accuracy of UAV SfM-MVS derived outputs.
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