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Remote Sens. 2016, 8(2), 104; doi:10.3390/rs8020104

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

Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editors: Diego Gonzalez-Aguilera, Norman Kerle and Prasad S. Thenkabail
Received: 30 November 2015 / Revised: 6 January 2016 / Accepted: 21 January 2016 / Published: 29 January 2016
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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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Habib, A.; Lin, Y.-J. Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data. Remote Sens. 2016, 8, 104.

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