Segmentation and classification of airborne laser scanner data

by George Sithole

Publisher: Nederlandse Commissie voor Geodesie in Delft

Written in English
Published: Pages: 184 Downloads: 269
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Subjects:

  • High power lasers.,
  • Computer vision.,
  • Image analysis -- Data processing.,
  • Remote sensing -- Data processing.

Edition Notes

StatementGeorge Sithole.
SeriesPublications in geodesy -- 59, Publications on geodesy (Nederlandse Commissie voor Geodesie) -- 50
The Physical Object
Paginationv, 184 p. :
Number of Pages184
ID Numbers
Open LibraryOL20095598M
ISBN 109061322928

Existing 2D GIS data may be used in the process to provide a reliable segmentation of airborne laser scanner datasets and to generate hypothesis supporting the actual building reconstruction. The method of 3D building model generation based on airborne laser scanner data and 2D GIS data has been tested on a dataset.   Segmentation and classification of 3D urban point clouds is a complex task, making it very difficult for any single method to overcome all the diverse challenges offered. This . Airborne and Terrestrial Laser Scanning is organized into nine chapters grouped into three distinct topics. The first is a detailed description of principals of laser scanners, light-measuring methods, components and properties of a laser scanner, operational considerations, data handling and storage, and system geometry and calibration. This paper presents the framework for a recently developed unsupervised classification algorithm called Skewness Balancing for object and ground point separation in airborne LIDAR data. The main advantages of the algorithm are threshold-freedom and independence from LIDAR data format and resolution, while preserving object and terrain details.

KEY WORDS: Airborne laser scanner, image processing, segmentation, classification ABSTRACT Airborne laser scanning data has proven to be a very suitable technique for the determination of digital surface models and is more and more being used for mapping and GIS data acquisition purposes, including the detection and modeling. Keywords: Airborne Laser Scanning, Waveform, Calibration, Segmentation, Vegetation 1. Introduction Small-footprint Airborne Laser Scanning (ALS) has evolved to a state-of-the-art technique for topographic data retrieval with major utilization in Digital Terrain Model (DTM) generation. Airborne Remote Sensing Data Collection and Pre‐processing. Airborne laser scanning data were acquired on 7–9 September , using a Riegl LMS‐Qi sensor (RIEGL Laser Measurement Systems GmbH, Horn, Austria). The scan frequency was kHz and up to four returns were recorded. The average point density was of 48 pts m −2. A digital. In this paper, an automatic approach for the generation and regularization of 3D roof boundaries in Airborne Laser scanner data is presented. The workflow is commenced by segmentation of the point clouds. A classification step and a rule based roof extraction step are followed the planar segmentation. Refinement on roof extraction is performed in order to minimize the effect due to urban.

Airborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning. This article is from Sensors (Basel, Switzerland), volume ctAirborne laser scanning (ALS) is a remote sensing technique well-suited for 3D vegetation. 1. Introduction. Object segmentation and classification are widely researched topics in surveying, mapping, and autonomous navigation by mobile robots [1,2].These techniques allow a robot to navigate through and interact with its environment by providing quickly accessible and accurate information regarding the surrounding terrain [].The multiple sensors mounted on such robots collect terrain.

Segmentation and classification of airborne laser scanner data by George Sithole Download PDF EPUB FB2

Classification of point clouds is needed as a first step in the extraction of various types of geo-information from point clouds. We present a new approach to contextual classification of segmented airborne laser scanning data. Potential advantages of segment-based classification are easily offset by segmentation by: Request PDF | On Jan 1,G.

Sithole published Segmentation and Classification of Airborne Laser Scanner Data | Find, read and cite all the research you need on ResearchGateAuthor: George Sithole.

A two-step approach applied to airborne laser scanning point cloud s has been presented by Xu et al. After an initial segmentation and classification of planar point. Download Citation | On Jan 1,Dániel Tóvári published Segmentation Based Classification of Airborne Laser Scanner Data | Find, read and cite all the research you need on ResearchGate.

Classification of airborne laser scanning data using JointBoost the contextual constraints for objects extracted by graph-cut segmentation are used to optimize the initial classification results obtained by the JointBoost classifier. Our experiments indicate that the proposed features and method are effective for classification of Cited by: In this paper, a number of techniques for segmentation and classification of airborne laser scanner data are presented.

First, a method for ground estimation is described, that is based on region growing starting from a set of ground seed points. MAP BASED SEGMENTATION OF AIRBORNE LASER SCANNER DATA. Wanga, S.J. Oude Elberink a, * a Faculty of Geo -Information Science and Earth Observation, University of Twente, Netherlands [email protected], [email protected] Classification of point clouds is needed as a first step in the extraction of various types of geo-information from point clouds.

We present a new approach to contextual classification of segmented airborne laser scanning data. Potential advantages of segment-based classification are easily offset by segmentation errors. We combine different point cloud segmentation methods to minimise both.

1. Introduction. Airborne LiDAR is becoming an important technology for the acquisition of highly accurate dense point cloud.

Nowadays, the latest devices, such as the Leica ALS70 LiDAR system, can provide a lateral accuracy of better than 12 cm and a vertical accuracy of better than 7 cm at a typical flying height of m (Leica Inc., ).The density of an airborne point cloud is closely.

The scanning was carried out using FLI-MAP laser scanner mounted on a helicopter. FLI-MAP uses multiple pulse in air technique which allows laser scanner to operate at higher scanning frequency. Airborne laser data campaign was held between and and the data is made publicly available.

N2 - Classification of point clouds is needed as a first step in the extraction of various types of geo-information from point clouds. We present a new approach to contextual classification of segmented airborne laser scanning data. Potential advantages of segment-based classification are easily offset by segmentation errors.

Airborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic.

tions and saw log recoveries of boreal forests from airborne laser scanning data and aerial photographs: a distribution-based approach.

Silva Fennica 42(4): – The low-density airborne laser scanning (ALS) data based estimation methods have been shown to produce accurate estimates of mean forest characteristics and diameter.

There is applied the object-oriented classification based on multispectral images with and without the combination with airborne laser scanning data in the eCognition Developer 9 software. In accordance to the comparison of classification results, the using of the airborne laser scanning data allowed identifying ground of terrain and the.

attribute space. This discussion is followed by an introduction of the proposed method for the classification of the segmentation results.

Afterwards, experimental results with real data are presented to verify the feasibility of the proposed approach for terrain and off-terrain classification of airborne and terrestrial laser scanning data.

A hierarchical classification method for Airborne Laser Scanning (ALS) data of urban areas is proposed in this paper. This method is composed of three stages among which three types of primitives are utilized, i.e., smooth surface, rough surface, and individual point.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Airborne laserscanning is being used for an increasing number of mapping and GIS data ac-quisition tasks. Besides the original purpose of digital terrain model generation, new applications arise in the automatic detection and modeling of objects such as buildings or vegetation for the generation of 3-D city models.

KEY WORDS: 3D laser scanning, point cloud, classification, segmentation ABSTRACT With the use of terrestrial laser scanning, it is possible to capture thousands of 3-dimensional points on the surface of an object.

The problem is that the vast quantity of data that needs to be manipulated sometimes hinders its application. There exist several.

This research proposes an automated approach for building reconstruction from airborne laser scanning data. We use a reliable segmentation algorithm to separate roads, trees and meadows from.

KEY WORDS: Supervised classification, maximum entropy modelling, rule based classification, airborne laser scanner data, segmentation, object-based point cloud analysis ABSTRACT: Rapid mapping of damaged regions and individual buildings is essential for efficient crisis management.

Airborne laser scanner. Rapid mapping of damaged regions and individual buildings is essential for efficient crisis management. Airborne laser scanner (ALS) data is potentially able to deliver accurate information on the 3D structures in a damaged region. In this paper we describe two different strategies how to process ALS point clouds in order to detect collapsed buildings automatically.

This book provides a comprehensive discussion of topographic LiDAR principles, systems, data acquisition, and data processing techniques. Ranging and scanning fundamentals, and broad, contemporary analysis of airborne LiDAR systems, as well as those situated on land and in.

Laser scanning is the principal technology for efficient 3D data capture in the form of point clouds. Point clouds can be generated from laser scanners or derived from image matching techniques, although the focus in this Special Issue is on laser scanner point clouds.

However, a point is just a point. Segmentation and classification of airborne laser scanner data: Author: Sithole, G. Thesis advisor: Vosselman, M.G. Date issued: Access: Open Access: Reference(s) Lidar, filtering: Language: English: Type: Doctoral Thesis: Abstract: Various methods have been developed to measure the physical presence of objects in a landscape with.

1. Introduction. Airborne laser scanning (ALS) is commonly used for high resolution digital terrain model (DTM) derivation [],[], but became also an important tool for object classification and parameter estimation for several applications such as in forestry and in urban g work in forestry focuses on the delineation of stands and single trees and their parameterization [],[].

Segmentation and classification of airborne laser scanner data. Author. Sithole, G. Contributor. Vosselman, M.G. (promotor) Faculty. Aerospace Engineering. Date. Abstract. Various methods have been developed to measure the physical presence of objects in a landscape with high positional accuracy.

Airborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes.

3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction. With airborne laser scanning points are measured on the terrain surface, and on other objects as buildings and vegetation.

With socalled filtering methods a classification of the points into terrain and object points is performed. In the literature two approaches – i.e. a general strategies for solving the problem – for filtering can be identified. The first work directly on the measured.

To reduce the three-dimensional accumulator array to two dimensions, the slopes of the normal vector in the x and y directions were used as attributes for the planar patch segmentation, rather than using all three normal vector components to handle airborne laser scanning data [22,23,31].

Even though this method reduces the dimensions of the. introduces a new method for glacier surface segmentation using solely Airborne Laser Scanning data and outlines an object-based surface classification approach. The segmentation algorithm utilizes both, spatial (x,y,z) and brightness information (signal intensity) of the unstructured point cloud.

Airborne laser scanning (ALS) has been suggested and tested as a tool for this purpose and in the present study a novel procedure for identification and segmentation of small trees is proposed. The study was carried out in the Rollag municipality in southeastern Norway, where ALS data and field measurements of individual trees were acquired.available through airborne laser scanning (ALS) offer increased potential for 3D object segmentation.

Such potential is further augmented by the availability of full-waveform (FWF) ALS data. FWF ALS has demonstrated enhanced performance in segmentation and classification through the additional physical observables which can be provided alongside.Keywords: Object-based Point Cloud Analysis, Urban Vegetation, Segmentation, Classification 1 INTRODUCTION Currently airborne laser scanning (ALS) data is not only used for scientific investigations but also for operational applications.

Several area-wide flight campaigns generate basic geo and remote sensing data sets, which are hosted by public.