Automatic road extraction from LIDAR data based on classifier fusion

Samadzadegan, Farhad; Hahn, Michael; Bigdeli, Behnaz

doi:10.1109/urs.2009.5137739

Cited by 35 publications

(23 citation statements)

References 5 publications

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“…Other approaches have been based on extracting road and its environment from LiDAR data. Clode et al (2004) and Hu et al (2004) segmented Airborne Laser Scanning (ALS) data into road and non-road based on elevation and intensity attributes, while Samadzadegan et al (2009) used first echo, last echo, range and intensity attributes to classify the ALS points into road objects. Mumtaz and Mooney (2009) used ALS elevation and intensity attributes to extract spatial information about buildings, trees, roads, poles and wires in the route corridor environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated road markings extraction from mobile laser scanning data

Kumar

McElhinney

Lewis

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

108

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a b s t r a c tRoad markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost-effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated target to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Automated road markings extraction from mobile laser scanning data

Kumar

McElhinney

Lewis

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

108

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“…Numerous studies have focused on extracting one object type in urban scenes, such as separation of ground from non-ground points in order to generate digital terrain models (DEMs) (Bartels et al 2006), building extraction (Huang et al, 2013), road extraction (Samadzadegan et al, 2009) and curbstones mapping (Zhou and Vosselman, 2012). The number of extracted classes has been extended to three main urban classes, including ground, vegetation and buildings (Samadzadegan et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Morsy

Shaker

El-Rabbany

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the evolution of the LiDAR technology, multispectral airborne laser scanning systems are currently available. The first operational multispectral airborne LiDAR sensor, the Optech Titan, acquires LiDAR point clouds at three different wavelengths (1.550, 1.064, 0.532 m), allowing the acquisition of different spectral information of land surface. Consequently, the recent studies are devoted to use the radiometric information (i.e., intensity) of the LiDAR data along with the geometric information (e.g., height) for classification purposes. In this study, a data clustering method, based on Gaussian decomposition, is presented. First, a ground filtering mechanism is applied to separate non-ground from ground points. Then, three normalized difference vegetation indices (NDVIs) are computed for both non-ground and ground points, followed by histograms construction from each NDVI. The Gaussian function model is used to decompose the histograms into a number of Gaussian components. The maximum likelihood estimate of the Gaussian components is then optimized using Expectation -Maximization algorithm. The intersection points of the adjacent Gaussian components are subsequently used as threshold values, whereas different classes can be clustered. This method is used to classify the terrain of an urban area in Oshawa, Ontario, Canada, into four main classes, namely roofs, trees, asphalt and grass. It is shown that the proposed method has achieved an overall accuracy up to 95.1% using different NDVIs.

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“…(Grote, et al, 2012). Intensive research has been conducted on automatic road extraction from VHR optical images (Mayer, et al, 2006), SAR images (Hedman, et al, 2004;Saati, et al, 2015), LiDAR data (Samadzadegan, et al, 2009) and on the integration of different data sources (Rahimi, et al, 2015). Nevertheless it is still one of the important and challenging subjects in urban remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Objects Grouping for Segmentation of Roads Network in High Resolution Images of Urban Areas

Maboudi¹,

Amini²,

Hahn³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Updated road databases are required for many purposes such as urban planning, disaster management, car navigation, route planning, traffic management and emergency handling. In the last decade, the improvement in spatial resolution of VHR civilian satellite sensors -as the main source of large scale mapping applications -was so considerable that GSD has become finer than size of common urban objects of interest such as building, trees and road parts. This technological advancement pushed the development of "Object-based Image Analysis (OBIA)" as an alternative to pixel-based image analysis methods. Segmentation as one of the main stages of OBIA provides the image objects on which most of the following processes will be applied. Therefore, the success of an OBIA approach is strongly affected by the segmentation quality. In this paper, we propose a purposedependent refinement strategy in order to group road segments in urban areas using maximal similarity based region merging. For investigations with the proposed method, we use high resolution images of some urban sites. The promising results suggest that the proposed approach is applicable in grouping of road segments in urban areas.

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Automatic road extraction from LIDAR data based on classifier fusion

Cited by 35 publications

References 5 publications

Automated road markings extraction from mobile laser scanning data

Automated road markings extraction from mobile laser scanning data

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Objects Grouping for Segmentation of Roads Network in High Resolution Images of Urban Areas

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