Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation

Susaki, Junichi

doi:10.3390/rs4061804

Cited by 135 publications

(71 citation statements)

References 10 publications

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“…7. Repeating (5)- (6). The simulation process will terminate when the maximum height variation (M_HV) of all particles is small enough or when it exceeds the maximum iteration number which is specified by the user.…”

Section: Implementation Of Csfmentioning

confidence: 99%

An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

et al. 2016

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Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing) working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily.

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Section: Implementation Of Csfmentioning

confidence: 99%

An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

et al. 2016

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“…Jahromi et al [46] employed the filter based on artificial neural networks. Susaki [47] filtered the urban datasets by an adaptive slope filtering method. Zhang and Lin [21] improved the progressive TIN densification filtering method by a point cloud segmentation using smoothness constraint.…”

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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“…Vosselman did the pioneering work [20] about slope-based filtering. Some extensions and variants of slope-based filters focus on the shape of structure elements [21], the determination of adaptive slope parameters [22], and topological analysis removing very large buildings [10]. The slope-based methods usually work well when object and terrain points are equally mixed.…”

Section: Previous Workmentioning

confidence: 99%

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

Lin

Zhang

2014

Remote Sensing

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Abstract:Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third step is our main contribution. Particularly, the iterative judgment is based on the framework of the classic progressive TIN (triangular irregular network) densification (PTD) method, but with basic processing unit being a segment rather than a single point. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. Experimental results suggest that, compared with the PTD method, the SBF approach is capable of preserving discontinuities of landscapes and removing the lower parts of large objects attached on the ground surface. As a result, the SBF approach is able to reduce omission errors and total errors by 18.26% and 11.47% respectively, which would significantly decrease the cost of manual operation required in post-processing.

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Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation

Cited by 135 publications

References 10 publications

An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

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