文章基本信息

标题：AUTOMATIC DETECTION AND CHARACTERIZATION OF GROUND OCCLUSIONS IN URBAN POINT CLOUDS FROM MOBILE LASER SCANNING DATA
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作者：J. Balado ; E. González ; E. Verbree 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2020
卷号：VI-4-W1-2020
页码：13-20
DOI：10.5194/isprs-annals-VI-4-W1-2020-13-2020
语种：English
出版社：Copernicus Publications
摘要：Occlusions accompany serious problems that reduce the applicability of numerous algorithms. The aim of this work is to detect and characterize urban ground gaps based on occluding object. The point clouds for input have been acquired with Mobile Laser Scanning and have been previously segmented into ground, buildings and objects, which have been classified. The method generates various raster images according to segmented point cloud elements, and detects gaps within the ground based on their connectivity and the application of the hit-or-miss transform. The method has been tested in four real case studies in the cities of Vigo and Paris, and an accuracy of 99.6% has been obtained in occlusion detection and labelling. Cars caused 80.6% of the occlusions. Each car occluded an average ground area of 11.9thinsp;msup2/sup. The proposed method facilitates knowing the percentage of occluded ground, and if this would be reduced in successive multi-temporal acquisitions based on mobility characteristics of each object class.