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Article

Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data

by and *,†
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Diego Gonzalez-Aguilera, Norman Kerle and Prasad S. Thenkabail
Remote Sens. 2016, 8(2), 104; https://doi.org/10.3390/rs8020104
Received: 30 November 2015 / Revised: 6 January 2016 / Accepted: 21 January 2016 / Published: 29 January 2016
3D modeling of a given site is an important activity for a wide range of applications including urban planning, as-built mapping of industrial sites, heritage documentation, military simulation, and outdoor/indoor analysis of airflow. Point clouds, which could be either derived from passive or active imaging systems, are an important source for 3D modeling. Such point clouds need to undergo a sequence of data processing steps to derive the necessary information for the 3D modeling process. Segmentation is usually the first step in the data processing chain. This paper presents a region-growing multi-class simultaneous segmentation procedure, where planar, pole-like, and rough regions are identified while considering the internal characteristics (i.e., local point density/spacing and noise level) of the point cloud in question. The segmentation starts with point cloud organization into a kd-tree data structure and characterization process to estimate the local point density/spacing. Then, proceeding from randomly-distributed seed points, a set of seed regions is derived through distance-based region growing, which is followed by modeling of such seed regions into planar and pole-like features. Starting from optimally-selected seed regions, planar and pole-like features are then segmented. The paper also introduces a list of hypothesized artifacts/problems that might take place during the region-growing process. Finally, a quality control process is devised to detect, quantify, and mitigate instances of partially/fully misclassified planar and pole-like features. Experimental results from airborne and terrestrial laser scanning as well as image-based point clouds are presented to illustrate the performance of the proposed segmentation and quality control framework. View Full-Text
Keywords: region growing; optimal seed region; local point density; quality control; planar and pole-like features; misclassified features region growing; optimal seed region; local point density; quality control; planar and pole-like features; misclassified features
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MDPI and ACS Style

Habib, A.; Lin, Y.-J. Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data. Remote Sens. 2016, 8, 104. https://doi.org/10.3390/rs8020104

AMA Style

Habib A, Lin Y-J. Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data. Remote Sensing. 2016; 8(2):104. https://doi.org/10.3390/rs8020104

Chicago/Turabian Style

Habib, Ayman, and Yun-Jou Lin. 2016. "Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data" Remote Sensing 8, no. 2: 104. https://doi.org/10.3390/rs8020104

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