Journal ArticleDOI
Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds
G. Sithole,George Vosselman +1 more
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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.read more
Citations
More filters
Journal ArticleDOI
An adaptive filtering algorithm of multilevel resolution point cloud
TL;DR: An adaptive filtering algorithm is proposed which is improved based on multilevel resolution algorithm which maintains advantages of high accuracy under complex topographic environment and can reach 90%.
Terrain Referenced Navigation Using SIFT Features in LiDAR Range-Based Data
TL;DR: In this paper, the authors used a single calibrated scanning LiDAR to sample the range and angle to the ground as an aircraft flies, forming a point cloud, which is then interpolated into a Digital Elevation Model (DEM) of the ground.
Proceedings ArticleDOI
Improving Automation in Map Updating Based on National Laser Scanning, Classification Trees, Object-Based Change Detection and 3D Object Reconstruction
TL;DR: The possibility to use countrywide collection of Laser data, possibly multi-temporal laser data, for updating Topographic and forest databases, especially concerning the detection of the changed buildings or trees and reconstructing them from laser scanner data is discussed.
Book ChapterDOI
3D Building Reconstruction from Airborne Lidar Point Clouds Fused with Aerial Imagery
Jonathan Li,Haiyan Guan +1 more
Journal ArticleDOI
LiDAR Point Cloud Data with Morphological Filter Algorithm Based on Region Prediction
TL;DR: Li et al. as discussed by the authors proposed a point cloud data filtering algorithm based on region prediction, which can effectively remove non-ground points, keep the ground points and is effective at minimizing total error rates.
References
More filters
Journal ArticleDOI
Determination of terrain models in wooded areas with airborne laser scanner data
K. Kraus,Norbert Pfeifer +1 more
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.