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

2016 ◽  
Vol 8 (6) ◽  
pp. 501 ◽  
Author(s):  
Wuming Zhang ◽  
Jianbo Qi ◽  
Peng Wan ◽  
Hongtao Wang ◽  
Donghui Xie ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Data Filtering ◽  
Cloth Simulation ◽  
Filtering Method ◽  
Airborne Lidar Data

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

scholarly journals REVISED PROGRESSIVE MORPHOLOGICAL METHOD FOR GROUND POINT CLASSIFICATION OF AIRBORNE LIDAR DATA

2019 ◽  
Vol 6 (1-2) ◽  
pp. 31-38 ◽  
Author(s):  
Mohd Radhie Mohd Salleh ◽  
Muhammad Zulkarnain Abd Rahman ◽  
Zamri Ismail ◽  
Mohd Faisal Abdul Khanan ◽  
Mohd Asraff Asmadi
Keyword(s):  
Survey Data ◽  
Digital Terrain Model ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Major Step ◽  
Forest Environment ◽  
Local Slope ◽  
Filtering Method ◽  
Ground Survey ◽  
Airborne Lidar Data

Airborne Light Detection and Ranging (LiDAR) has been very effectively used in collecting terrain information over different scales of area. Inevitably, filtering the non-ground returns is the major step of digital terrain model (DTM) generation and this step poses the greatest challenge especially for tropical forest environment which consists of steep undulating terrain and mostly covered by a relatively thick canopy density. The aim of this research is to assess the performance of the Progressive Morphological (PM) algorithm after the implementation of local slope value in the ground filtering process. The improvement on the PM filtering method was done by employing local slope values obtained either using initial filtering of airborne LiDAR data or ground survey data. The filtering process has been performed with recursive mode and it stops after the results of the filtering does not show any improvement and the DTM error larger than the previous iteration. The revised PM filtering method has decreasing pattern of DTM error with increasing filtering iterations with minimum ±0.520 m of RMSE value. The results also suggest that spatially distributed slope value applied in PM filtering algorithm either from LiDAR ground points or ground survey data is capable in preserving discontinuities of terrain and correctly remove non-terrain points especially in steep area.

scholarly journals Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology

2017 ◽  
Vol 9 (11) ◽  
pp. 1104 ◽  
Author(s):  
Yong Li ◽  
Bin Yong ◽  
Peter van Oosterom ◽  
Mathias Lemmens ◽  
Huayi Wu ◽  
...  
Keyword(s):  
Mathematical Morphology ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Data Filtering ◽  
Airborne Lidar Data

scholarly journals Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters

2019 ◽  
Vol 11 (9) ◽  
pp. 1037 ◽  
Author(s):  
Shangshu Cai ◽  
Wuming Zhang ◽  
Xinlian Liang ◽  
Peng Wan ◽  
Jianbo Qi ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Point Clouds ◽  
Parameter Tuning ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Terrain Model ◽  
Adaptive Parameter ◽  
Digital Terrain ◽  
Airborne Lidar Data

Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space

2021 ◽  
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
André Caceres Carrilho ◽  
Guilherme Gomes Pessoa
Keyword(s):  
Urban Space ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Multi Temporal ◽  
Airborne Lidar Data
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