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Vehicle detection from airborne LiDAR point clouds based on a decision tree algorithm with horizontal and vertical features

Title
Vehicle detection from airborne LiDAR point clouds based on a decision tree algorithm with horizontal and vertical features
Authors
Eum J.Bae M.Jeon J.Lee H.Oh S.Lee M.
Ewha Authors
이민수
SCOPUS Author ID
이민수scopus
Issue Date
2017
Journal Title
Remote Sensing Letters
ISSN
2150-704XJCR Link
Citation
vol. 8, no. 5, pp. 409 - 418
Keywords
decision treeLiDAR point cloudsOBPCAvehicle detection
Publisher
Taylor and Francis Ltd.
Indexed
SCI; SCIE; SCOPUS WOS scopus
Abstract
The object-based point cloud analysis (OBPCA) method has been used for vehicle detection from airborne light detection and ranging (LiDAR) point clouds with a relatively simple process and exhibits a degree of accuracy as high as that of a three-dimensional point cloud-based detection scheme. However, it only utilizes horizontal features of the segmented point clouds, and it uses thresholds established by heuristic observation and experience. In this article, we present a novel method for vehicle detection from airborne LiDAR point clouds based on a decision tree algorithm with horizontal and vertical features. It calculates the horizontal and vertical features for segments created by the filtering and segmentation processes, and it establishes a vehicle detection model by training a decision tree classifier with horizontal and vertical features of the segments. Our experiment shows that our proposed method outperforms the previous method in terms of recall and precision by 13.14% and 30.02%, respectively. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
DOI
10.1080/2150704X.2016.1278310
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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