Computationally Efficient Method for the Generation of a Digital Terrain Model From Airborne LiDAR Data Using Connected Operators

Mongus, Domen; Žalik, Borut

doi:10.1109/jstars.2013.2262996

Cited by 58 publications

(33 citation statements)

References 31 publications

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“…Comparing with other popular approaches, Li et al [44] obtains the reliable and competitive performance for varied survey areas. Additionally, the comparison is also made with the filtering performance of the widely used commercial software Terrasolid TerraScan, which requires manual tuning of relevant parameters for different environments [40,45]. The comparison of filtering results is shown as Figures 7-9.…”

Section: Resultsmentioning

confidence: 99%

Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology

Yong

Oosterom

et al. 2017

Remote Sensing

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The capability of acquiring accurate and dense three-dimensional geospatial information that covers large survey areas rapidly enables airborne light detection and ranging (LiDAR) has become a powerful technology in numerous fields of geospatial applications and analysis. LiDAR data filtering is the first and essential step for digital elevation model generation, land cover classification, and object reconstruction. The morphological filtering approaches have the advantages of simple concepts and easy implementation, which are able to filter non-ground points effectively. However, the filtering quality of morphological approaches is sensitive to the structuring elements that are the key factors for the filtering success of mathematical operations. Aiming to deal with the dependence on the selection of structuring elements, this paper proposes a novel filter of LiDAR point clouds based on geodesic transformations of mathematical morphology. In comparison to traditional morphological transformations, the geodesic transformations only use the elementary structuring element and converge after a finite number of iterations. Therefore, this algorithm makes it unnecessary to select different window sizes or determine the maximum window size, which can enhance the robustness and automation for unknown environments. Experimental results indicate that the new filtering method has promising and competitive performance for diverse landscapes, which can effectively preserve terrain details and filter non-ground points in various complicated environments.

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Section: Resultsmentioning

confidence: 99%

Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology

Yong

Oosterom

et al. 2017

Remote Sensing

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“…TerraScan uses the TIN-based filtering method. This software produces a significantly low average total error when a set of tunable parameters of the data is passed to the algorithm [42]. Compared with the tested algorithms, the proposed SGF produces the second type I error and total error, as indicated in Table 1.…”

Section: Quality Assessment On Isprs Test Data Setmentioning

confidence: 96%

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

Pang

et al. 2015

Remote Sensing

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Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data with diverse scenes. This paper proposes a fast and intelligent algorithm called Semi-Global Filtering (SGF). The SGF models the filtering as a labeling problem in which the labels correspond to possible height levels. A novel energy function balanced by adaptive ground saliency is employed to adapt to steep slopes, discontinuous terrains, and complex objects. Semi-global optimization is used to determine labels that minimize the energy. These labels form an optimal classification surface based on which the points are classified as either ground or non-ground. The experimental results show that the SGF algorithm is very efficient and able to produce high classification accuracy. Given that the major procedure of semi-global optimization using dynamic programming is conducted independently along eight directions, SGF can also be paralleled and sped up via Graphic Processing Unit computing, which runs at a speed of approximately 3 million points per second. OPEN ACCESSRemote Sens. 2015, 7 10997

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“…al., 2015;Meng et. al., 2010;Mongus et. al., 2014;Bartels and Wei, 2010;Sithole and Vosselman, 2004;Zhang and Whitman, 2005;Liu, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…al., 2003;Chen et. al., 2007;Mongus et. al., 2014;Arefi and Hahn, 2005;Mongus and Zalik, 2012;Zhang and Whitman, 2005;Pingel et.…”

Section: Introductionmentioning

confidence: 99%

“…Ground filtering methods using global optimization techniques have also emerged in order to avoid some of the problems above that may arise due to the local nature of many algorithms (Elmqvist, 2002;Mongus and Žalik, 2014;Zhou and Neumann, 2013). Such methods take global features of the terrain into consideration as well as the local ones.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Min-Cut Based Filter for Airborne Lidar Data

Ural¹,

Shan²

2016

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

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ABSTRACT:LiDAR (Light Detection and Ranging) is a routinely employed technology as a 3-D data collection technique for topographic mapping. Conventional workflows for analyzing LiDAR data require the ground to be determined prior to extracting other features of interest. Filtering the terrain points is one of the fundamental processes to acquire higher-level information from unstructured LiDAR point data. There are many ground-filtering algorithms in literature, spanning several broad categories regarding their strategies. Most of the earlier algorithms examine only the local characteristics of the points or grids, such as the slope, and elevation discontinuities. Since considering only the local properties restricts the filtering performance due to the complexity of the terrain and the features, some recent methods utilize global properties of the terrain as well. This paper presents a new ground filtering method, Min-cut Based Filtering (MBF), which takes both local and global properties of the points into account. MBF considers ground filtering as a labeling task. First, an energy function is designed on a graph, where LiDAR points are considered as the nodes on the graph that are connected to each other as well as to two auxiliary nodes representing ground and off-ground labels. The graph is constructed such that the data costs are assigned to the edges connecting the points to the auxiliary nodes, and the smoothness costs to the edges between points. Data and smoothness terms of the energy function are formulated using point elevations and approximate ground information. The data term conducts the likelihood of the points being ground or off-ground while the smoothness term enforces spatial coherence between neighboring points. The energy function is optimized by finding the minimumcut on the graph via the alpha-expansion algorithm. The resulting graph-cut provides the labeling of the point cloud as ground and off-ground points. Evaluation of the proposed method on the ISPRS test dataset for ground filtering demonstrates that the results are comparable with most current existing methods. An overall average filtering accuracy for the 15 ISPRS test areas is 91.3%.

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Computationally Efficient Method for the Generation of a Digital Terrain Model From Airborne LiDAR Data Using Connected Operators

Cited by 58 publications

References 31 publications

Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology

Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

A Min-Cut Based Filter for Airborne Lidar Data

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