Filtering LiDAR Data Using Binary Quadric Trend Surface and Morphological Approaches

2012 ◽  
Vol 594-597 ◽  
pp. 2361-2366 ◽  
Author(s):  
Feng Li ◽  
Xi Min Cui ◽  
Ling Zhang ◽  
Shu Wei Shan ◽  
Kun Lun Song
Keyword(s):  
Point Clouds ◽  
Experimental Results ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Data Sets ◽  
Type I ◽  
Morphological Filter ◽  
Light Detection ◽  
Trend Surface ◽  
Sample Data

Automatically identifying and removing above-ground laser points from terrain surface is proved to be a challenging task for complicated and discontinuous scenarios. Eight methods have been developed and contrasted with each other for filtering LiDAR (Light Detection and Ranging) data. Only one approach is difficult to acquire high precisions for various landscapes. This paper presents a method filtering point clouds in which firstly a binary quadric trend surface is used to remove most non-terrain points by a defined height threshold and subsequently a progressive morphological filter further is employed to detect ground measurements. The experimental results demonstrate that this method yields less type I and total errors compared with other eight approaches based on ISPRS sample data sets.

scholarly journals Training a terrain traversability classifier for a planetary rover through simulation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773540 ◽  
Author(s):  
Robert A Hewitt ◽  
Alex Ellery ◽  
Anton de Ruiter
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Data Sets ◽  
Light Detection ◽  
Planetary Rover ◽  
Training Methodology ◽  
Sensor Model ◽  
Random Fashion ◽  
Manual Classification

A classifier training methodology is presented for Kapvik, a micro-rover prototype. A simulated light detection and ranging scan is divided into a grid, with each cell having a variety of characteristics (such as number of points, point variance and mean height) which act as inputs to classification algorithms. The training step avoids the need for time-consuming and error-prone manual classification through the use of a simulation that provides training inputs and target outputs. This simulation generates various terrains that could be encountered by a planetary rover, including untraversable ones, in a random fashion. A sensor model for a three-dimensional light detection and ranging is used with ray tracing to generate realistic noisy three-dimensional point clouds where all points that belong to untraversable terrain are labelled explicitly. A neural network classifier and its training algorithm are presented, and the results of its output as well as other popular classifiers show high accuracy on test data sets after training. The network is then tested on outdoor data to confirm it can accurately classify real-world light detection and ranging data. The results show the network is able to identify terrain correctly, falsely classifying just 4.74% of untraversable terrain.

scholarly journals Hyperspectral and LiDAR Data Fusion Classification Using Superpixel Segmentation-Based Local Pixel Neighborhood Preserving Embedding

2019 ◽  
Vol 11 (5) ◽  
pp. 550 ◽  
Author(s):  
Yunsong Li ◽  
Chiru Ge ◽  
Weiwei Sun ◽  
Jiangtao Peng ◽  
Qian Du ◽  
...  
Keyword(s):  
Experimental Data ◽  
Data Fusion ◽  
Experimental Results ◽  
New Method ◽  
Light Detection And Ranging ◽  
Actual Data ◽  
Lidar Data ◽  
Light Detection ◽  
Superpixel Segmentation ◽  
Open Standard

A new method of superpixel segmentation-based local pixel neighborhood preserving embedding (SSLPNPE) is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR) data based on the extinction profiles (EPs), superpixel segmentation and local pixel neighborhood preserving embedding (LPNPE). A new workflow is proposed to calibrate the Goddard’s LiDAR, hyperspectral and thermal (G-LiHT) data, which allows our method to be applied to actual data. Specifically, EP features are extracted from both sources. Then, the derived features of each source are fused by the SSLPNPE. Using the labeled samples, the final label assignment is produced by a classifier. For the open standard experimental data and the actual data, experimental results prove that the proposed method is fast and effective in hyperspectral and LiDAR data fusion.

scholarly journals Object-Oriented Classification of Forest Structure from Light Detection and Ranging Data for Stand Mapping

2009 ◽  
Vol 24 (4) ◽  
pp. 198-204 ◽  
Author(s):  
Alicia A. Sullivan ◽  
Robert J. McGaughey ◽  
Hans-Erik Andersen ◽  
Peter Schiess
Keyword(s):  
High Resolution ◽  
Forest Structure ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Object Oriented ◽  
Light Detection And Ranging ◽  
Classification Error ◽  
Lidar Data ◽  
Data Sets ◽  
Light Detection

Abstract Stand delineation is an important step in the process of establishing a forest inventory and provides the spatial framework for many forest management decisions. Many methods for extracting forest structure characteristics for stand delineation and other purposes have been researched in the past, primarily focusing on high-resolution imagery and satellite data. High-resolution airborne laser scanning offers new opportunities for evaluating forests and conducting forest inventory. This study investigates the use of information derived from light detection and ranging (LIDAR) data as a potential tool for delineation of forest structure to create stand maps. Delineation methods are developed and tested using data sets collected over the Blue Ridge study site near Olympia, Washington. The methodology developed delineates forest areas using LIDAR data and object-oriented image segmentation and supervised classification. Error matrices indicate classification accuracies with a kappa hat values of 78 and 84% for 1999 and 2003 data sets, respectively.

scholarly journals A Novel Method for Feature Extraction and Classification for an Aerial Image and LiDAR Data with Genetic Algorithm

2012 ◽  
pp. 264-270
Author(s):  
Manjunath B. E ◽  
D. G. Anand ◽  
Mahant. G. Kattimani
Keyword(s):  
Feature Extraction ◽  
Light Detection And Ranging ◽  
Aerial Image ◽  
Lidar Data ◽  
Light Detection ◽  
Aerial Photos ◽  
The Earth ◽  
Digital Elevation ◽  
Land Surveying ◽  
Novel Method

Airborne Light Detection and Ranging (LiDAR) provides accurate height information for objects on the earth, which makes LiDAR become more and more popular in terrain and land surveying. In particular, LiDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. Aerial photos with LiDAR data were processed with genetic algorithms not only for feature extraction but also for orthographical image. DSM provided by LiDAR reduced the amount of GCPs needed for the regular processing, thus the reason both efficiency and accuracy are highly improved. LiDAR is an acronym for Light Detection and Ranging, which is typically defined as an integration of three technologies into a single system, which is capable of acquiring a data to produce accurate Digital Elevation Models.

scholarly journals AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES

2016 ◽  
Vol XLI-B2 ◽  
pp. 35-42 ◽  
Author(s):  
A. W. Lyda ◽  
X. Zhang ◽  
C. L. Glennie ◽  
K. Hudnut ◽  
B. A. Brooks
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Near Field ◽  
Three Dimensional ◽  
Light Detection And Ranging ◽  
Data Sets ◽  
Light Detection ◽  
Icp Algorithm ◽  
Detection Algorithms ◽  
Change Detection Algorithms

Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.

scholarly journals Tunnel Vision

2016 ◽  
Vol 4 (2) ◽  
pp. 192-204 ◽  
Author(s):  
Thomas G. Garrison ◽  
Dustin Richmond ◽  
Perry Naughton ◽  
Eric Lo ◽  
Sabrina Trinh ◽  
...  
Keyword(s):  
3D Models ◽  
Present Form ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Technical Expertise ◽  
Ancient Maya ◽  
Light Detection ◽  
Terrestrial Lidar ◽  
Tunnel Vision ◽  
El Zotz

AbstractArchaeological tunneling is a standard excavation strategy in Mesoamerica. The ancient Maya built new structures atop older ones that were no longer deemed usable, whether for logistical or ideological reasons. This means that as archaeologists excavate horizontal tunnels into ancient Maya structures, they are essentially moving back in time. As earlier constructions are encountered, these tunnels may deviate in many directions in order to document architectural remains. The resultant excavations often become intricate labyrinths, extending dozens of meters. Traditional forms of archaeological documentation, such as photographs, plan views, and profile drawings, are limited in their ability to convey the complexity of tunnel excavations. Terrestrial Lidar (light detection and ranging) instruments are able to generate precise 3D models of tunnel excavations. This article presents the results of a model created with a Faro™ Focus 3D 120 Scanner of tunneling excavations at the site of El Zotz, Guatemala. The lidar data document the excavations inside a large mortuary pyramid, including intricately decorated architecture from an Early Classic (A.D. 300–600) platform buried within the present form of the structure. Increased collaboration between archaeologists and scholars with technical expertise maximizes the effectiveness of 3D models, as does presenting digital results in tandem with traditional forms of documentation.

scholarly journals REAL-TIME POWERLINE CORRIDOR INSPECTION BY EDGE COMPUTING OF UAV LIDAR DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 547-551 ◽  
Author(s):  
S. Pu ◽  
L. Xie ◽  
M. Ji ◽  
Y. Zhao ◽  
W. Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Point Clouds ◽  
Power Line ◽  
Edge Computing ◽  
Ground Vegetation ◽  
Lidar Data ◽  
Type I ◽  
Industry Standards ◽  
Potential Risks

<p><strong>Abstract.</strong> This paper presents an innovative power line corridor inspection approach using UAV LiDAR edge computing and 4G real real-time transmission. First, sample point clouds of power towers are manually classified and decomposed into components according to five mainstream tower types: T type, V type, n type, I type and owl head type. A deep learning AI agent, named “Tovos Age Agent” internally, is trained by supervised deep learning the sample data sets under a 3D CNN framework. Second, laser points of power line corridors are simultaneously classified into Ground, Vegetation, Tower, Cable, and Building types using semantic feature constraints during the UAV-borne LiDAR acquisition process, and then tower types are further recognized by Tovos Agent for strain span separation. Spatial and topological relations between Cable points and other types are analyzed according to industry standards to identify potential risks at the same time. Finally, all potential risks are organized as industry standard reports and transmitted onto central server via 4G data link, so that maintenance personal can be notified the risks as soon as possible. Tests on LiDAR data of 1000&amp;thinsp;KV power line show the promising results of the proposed method.</p>

scholarly journals Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds

2014 ◽  
Vol XL-5 ◽  
pp. 629-632 ◽  
Author(s):  
Y. Yu ◽  
J. Li ◽  
H. Guan ◽  
D. Zai ◽  
C. Wang
Keyword(s):  
Point Cloud ◽  
Euclidean Distance ◽  
Point Clouds ◽  
Shape Descriptor ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Automated Extraction ◽  
Normalized Cut ◽  
Model Driven ◽  
Automated Algorithm

This paper presents an automated algorithm for extracting 3D trees directly from 3D mobile light detection and ranging (LiDAR) data. To reduce both computational and spatial complexities, ground points are first filtered out from a raw 3D point cloud via blockbased elevation filtering. Off-ground points are then grouped into clusters representing individual objects through Euclidean distance clustering and voxel-based normalized cut segmentation. Finally, a model-driven method is proposed to achieve the extraction of 3D trees based on a pairwise 3D shape descriptor. The proposed algorithm is tested using a set of mobile LiDAR point clouds acquired by a RIEGL VMX-450 system. The results demonstrate the feasibility and effectiveness of the proposed algorithm.

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