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Verlagsausgabe
DOI: 10.5445/IR/1000081641
Veröffentlicht am 09.04.2018
Originalveröffentlichung
DOI: 10.5194/isprs-annals-IV-1-W1-157-2017

Geometric Features and their Relevance for 3D Point Cloud Classification

Weinmann, Martin; Jutzi, Boris; Mallet, Clément; Weinmann, Michael

Abstract:
In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (linear structures, planar structures and volumetric structures) as well as a reference labeling with respect to five semantic classes (Wire, Pole/Trunk, Fac¸ade, Ground a ... mehr


Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator ISSN: 2194-9042
URN: urn:nbn:de:swb:90-816413
KITopen ID: 1000081641
Erschienen in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Band 4
Heft 1W1
Seiten 157-164
Bemerkung zur Veröffentlichung ISPRS Hannover Workshop 2017 on High-Resolution Earth Imaging for Geospatial Information, HRIGI 2017, City Models, Roads and Traffic , CMRT 2017, Image Sequence Analysis, ISA 2017, European Calibration and Orientation Workshop, EuroCOW 2017; Hannover; Germany; 6 June 2017 through 9 June 2017
Schlagworte 3D, Point Cloud, Feature Extraction, Classification, Feature Relevance Assessment
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