D Change Detection Using Adaptive Thresholds Based on Local Point Cloud Density

Bibliographic Data

ID22034310
AuthorsDan Liu (0009-0003-1264-0160, East China University of Technology, corresponding author), Dajun Li (0000-0001-5814-374X, East China University of Technology), Meizhen Wang (0000-0002-4135-5073, Nanjing Normal University), Zhiming Wang (0000-0002-0056-1604, East China University of Technology)
Year2021
Volume10
Issue3
Pages127
Publication date2021-03-02
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10030127
OpenAlexW3134108842
LanguageEN
Citations received1
References cited30

In recent years, because of highly developed LiDAR (Light Detection and Ranging) technologies, there has been increasing demand for 3D change detection in urban monitoring, urban model updating, and disaster assessment. In order to improve the effectiveness of 3D change detection based on point clouds, an approach for 3D change detection using point-based comparison is presented in this paper. To avoid density variation in point clouds, adaptive thresholds are calculated through the k-neighboring average distance and the local point cloud density. A series of experiments for quantitative evaluation is performed. In the experiments, the influencing factors including threshold, registration error, and neighboring number of 3D change detection are discussed and analyzed. The results of the experiments demonstrate that the approach using adaptive thresholds based on local point cloud density are effective and suitable

Change detection · Geography · Lidar · Point cloud · Ranging · Remote sensing · Telecommunications · Computer Science · Mathematics · Remote Sensing and Land Use · Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture · Artificial Intelligence

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