Next Article in Journal
A Hyperspectral Imaging Approach to White Matter Hyperintensities Detection in Brain Magnetic Resonance Images
Previous Article in Journal
An Evaluation of Four MODIS Collection 6 Aerosol Products in a Humid Subtropical Region
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(11), 1172; https://doi.org/10.3390/rs9111172

A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 26 October 2017 / Revised: 11 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
(This article belongs to the Section Remote Sensing Image Processing)
Full-Text   |   PDF [12940 KB, uploaded 16 November 2017]   |  

Abstract

Object reconstruction from airborne LiDAR data is a hot topic in photogrammetry and remote sensing. Power fundamental infrastructure monitoring plays a vital role in power transmission safety. This paper proposes a heuristic reconstruction method for power pylons widely used in high voltage transmission systems from airborne LiDAR point cloud, which combines both data-driven and model-driven strategies. Structurally, a power pylon can be decomposed into two parts: the pylon body and head. The reconstruction procedure assembles two parts sequentially: firstly, the pylon body is reconstructed by a data-driven strategy, where a RANSAC-based algorithm is adopted to fit four principal legs; secondly, a model-driven strategy is used to reconstruct the pylon head with the aid of a predefined 3D head model library, where the pylon head’s type is recognized by a shape context algorithm, and their parameters are estimated by a Metropolis–Hastings sampler coupled with a Simulated annealing algorithm. The proposed method has two advantages: (1) optimal strategies are adopted to reconstruct different pylon parts, which are robust to noise and partially missing data; and (2) both the number of parameters and their search space are greatly reduced when estimating the head model’s parameters, as the body reconstruction results information about the original point cloud, and relationships between parameters are used in the pylon head reconstruction process. Experimental results show that the proposed method can efficiently reconstruct power pylons, and the average residual between the reconstructed models and the raw data was smaller than 0.3 m. View Full-Text
Keywords: airborne LiDAR; 3D pylon reconstruction; RANdom Sample Consensus (RANSAC); Metropolis–Hastings sampler; simulated annealing airborne LiDAR; 3D pylon reconstruction; RANdom Sample Consensus (RANSAC); Metropolis–Hastings sampler; simulated annealing
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhou, R.; Jiang, W.; Huang, W.; Xu, B.; Jiang, S. A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data. Remote Sens. 2017, 9, 1172.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top