A multi-directional ground filtering algorithm for airborne LIDAR

Meng, Xuelian; Wang, Le; Silván-Cárdenas, José Luis; Currit, Nate

doi:10.1016/j.isprsjprs.2008.09.001

Cited by 181 publications

(129 citation statements)

References 46 publications

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“…Perko et al (2015) developed a new DTM extraction algorithm for DSMs derived from very high resolution satellite images called Multi-directional and Slope Dependent (MSD). This algorithm is an extension of the directional filtering approach introduced by (Meng et al, 2009). The main idea of the MSD algorithm is to specify points in the derived DSM which are positioned on bare ground regions and eliminate all other nonground points.…”

Section: Previous Workmentioning

confidence: 99%

“…The slope angle as a measure introduced in (Meng et al, 2009;Perko et al, 2015) seems to be not suitable for the DTM generation based on DSMs with a resolution of less than 1m GSD. For this reason the slope angle measure will be excluded from the processing of DTM extraction in our approach and will be replaced by an alternative solution.…”

Section: Drawback 3: Slope Angle As a Measure In High Resolution Dsmsmentioning

confidence: 99%

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New DTM Extraction Approach From Airborne Images Derived DSM

Mousa

Helmholz

Belton

2017

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

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In this work, a new filtering approach is proposed for a fully automatic Digital Terrain Model (DTM) extraction from very high resolution airborne images derived Digital Surface Models (DSMs). Our approach represents an enhancement of the existing DTM extraction algorithm Multi-directional and Slope Dependent (MSD) by proposing parameters that are more reliable for the selection of ground pixels and the pixelwise classification. To achieve this, four main steps are implemented: Firstly, 8 well-distributed scanlines are used to search for minima as a ground point within a pre-defined filtering window size. These selected ground points are stored with their positions on a 2D surface to create a network of ground points. Then, an initial DTM is created using an interpolation method to fill the gaps in the 2D surface. Afterwards, a pixel to pixel comparison between the initial DTM and the original DSM is performed utilising pixelwise classification of ground and non-ground pixels by applying a vertical height threshold. Finally, the pixels classified as non-ground are removed and the remaining holes are filled. The approach is evaluated using the Vaihingen benchmark dataset provided by the ISPRS working group III / 4. The evaluation includes the comparison of our approach, denoted as Network of Ground Points (NGPs) algorithm, with the DTM created based on MSD as well as a reference DTM generated from LiDAR data. The results show that our proposed approach over performs the MSD approach.

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Section: Previous Workmentioning

confidence: 99%

Section: Drawback 3: Slope Angle As a Measure In High Resolution Dsmsmentioning

confidence: 99%

New DTM Extraction Approach From Airborne Images Derived DSM

Mousa

Helmholz

Belton

2017

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

View full text Add to dashboard Cite

show abstract

“…In this study, the elevation histogram method [22] is first used to exclude evident outliers. The Delaunay triangulation [23][24][25] is then applied to determine the less evident outliers.…”

Section: Data Preprocessingmentioning

confidence: 99%

Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

Pang

Wang³

et al. 2014

Remote Sensing

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Building change detection is useful for land management, disaster assessment, illegal building identification, urban growth monitoring, and geographic information database updating. This study proposes an automatic method that applies object-based analysis to multi-temporal point cloud data to detect building changes. The aim of this building change detection method is to identify areas that have changed and to obtain from-to information. In this method, the data are first preprocessed to generate two sets of digital surface models (DSMs), digital elevation models, and normalized DSMs from registered old and new point cloud data. Thereafter, on the basis of differential DSM, candidates for changed building objects are identified from the points in the smooth areas by using a connected component analysis technique. The random sample consensus fitting algorithm is then used to distinguish the true changed buildings from trees. The changed building objects are classified as "newly built", "taller", "demolished" or "lower" by using rule-based analysis. Finally, a test data set consisting of many buildings of different types in an 8.5 km 2 area is selected for the experiment. In the test data set, the method correctly detects 97.8% of buildings larger than 50 m 2 . The accuracy of the method is 91.2%. Furthermore, to decrease the workload of subsequent manual checking of the result, the confidence index for each changed object is computed on the basis of object features.

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“…Various research has been done on the automatic processing of point clouds collected by airborne scanners [1,2]. There are also many scientific studies on the detection of primitives or specifically-shaped objects in point clouds [3,4].…”

Section: Introductionmentioning

confidence: 99%

Catenary System Detection, Localization and Classification Using Mobile Scanning Data

Pastucha

2016

Remote Sensing

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This paper presents a new method for detecting, locating and classifying overhead contact systems (catenary systems) in point clouds collected by mobile mapping systems (MMS) on rail roads. Contrary to many other application types, railway embankments are highly regulated and standardized. Railway infrastructure geometric relations remain roughly unchanged within established regions and have similarities between them. The newly-developed method exploits both these characteristics, as well as the survey process. There are several steps in this approach. Firstly, it restricts the search for catenaries relative to the distance to registered MMS trajectory, then finds possible support structures according to the density of points above the track. Subsequently, the method verifies the structures' presence and classifies the points with the use of the RANSAC algorithm. It establishes the presence of cantilevers, as well as poles or structural beams, depending on the type of detected support structure. The method also determines the coordinates of the identified object on the ground. Finally, a classification is clarified with the use of a modified DBSCAN algorithm. The design method has been verified with data collected in four surveys where the cumulative length of the route was almost 90 km. Over 97% of support structures were correctly detected, and out of these, over 95% were completely classified.

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A multi-directional ground filtering algorithm for airborne LIDAR

Cited by 181 publications

References 46 publications

New DTM Extraction Approach From Airborne Images Derived DSM

New DTM Extraction Approach From Airborne Images Derived DSM

Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

Catenary System Detection, Localization and Classification Using Mobile Scanning Data

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