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Journal ArticleDOI

Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds

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TLDR
In general, filters that estimate local surfaces are found to perform best and should be directed towards the usage of additional data sources, segment-based classification, and self-diagnosis of filter algorithms.
Abstract
Over the past years, several filters have been developed to extract bare-Earth points from point clouds. ISPRS Working Group III/3 conducted a test to determine the performance of these filters and the influence of point density thereon, and to identify directions for future research. Twelve selected datasets have been processed by eight participants. In this paper, the test results are presented. The paper describes the characteristics of the provided datasets and the used filter approaches. The filter performance is analysed both qualitatively and quantitatively. All filters perform well in smooth rural landscapes, but all produce errors in complex urban areas and rough terrain with vegetation. In general, filters that estimate local surfaces are found to perform best. The influence of point density could not well be determined in this experiment. Future research should be directed towards the usage of additional data sources, segment-based classification, and self-diagnosis of filter algorithms.

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Citations
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Journal ArticleDOI

Error assessment in two lidar-derived TIN datasets

TL;DR: In this paper, an accuracy assessment of two lidar-derived elevation datasets was conducted in areas of rugged terrain, where data from 906 ground checkpoints in various land-cover types were collected in situ as reference points.

Test case on the quality analysis of structure from motion in airborne applications

TL;DR: In this article, a digital elevation model, acquired by airborne laser scanning, with an average point density of 4 points per square meter, is used for the statistical quality assessment of the elevation model generated by structure from motion.
Journal ArticleDOI

TERRA: Terrain Extraction from elevation Rasters through Repetitive Anisotropic filtering

TL;DR: A new filtering method called TERRA (Terrain Extraction from elevation Rasters through Repetitive Anisotropic filtering), which performed well in minimising Type I errors (false ground removal), while Type II errors occurred locally where vegetation was covering the terrace edges.
Journal ArticleDOI

3D Photogrammetry Point Cloud Segmentation Using a Model Ensembling Framework

TL;DR: The US Army is paying increased attention to the development of rapid three-dimensional (3D) reconstruction using photogrammetry and unmanned aerial vehicle (UAV) technologies for creating 3D models for battlefield reconstruction.
Journal ArticleDOI

Wedge-Filtering of Geomorphologic Terrestrial Laser Scan Data

Helmut Panholzer, +1 more
- 20 Feb 2013 - 
TL;DR: The present article describes a method specifically suitable for filtering terrestrial laser scanning data based on the direct line of sight between the scanner and the measured point and the assumption that no other surface point can be located in the area above this connection line.
References
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Journal ArticleDOI

Determination of terrain models in wooded areas with airborne laser scanner data

TL;DR: In this article, the characteristics of laser scanning are compared to photogrammetry with reference to a big pilot project and the results are in accordance with the expectations, however, the geomorphologic quality of the contours, computed from a terrain model derived from laser scanning, needs to be improved.
Journal ArticleDOI

Processing of laser scanner data-algorithms and applications

TL;DR: This paper presents some methods and algorithms concerning filtering for determining the ground surface, DEM, classification of buildings for 3D City Models and the detection of electrical power lines.

Slope based filtering of laser altimetry data

TL;DR: In this article, a new method is proposed for filtering laser data, which is closely related to the erosion operator used for mathematical grey scale morphology, based on height differences in a representative training dataset, filter functions are derived that either preserve important terrain characteristics or minimise the number of classification errors.
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