Multi-Level Interpolation-Based Filter for Airborne LiDAR Point Clouds in Forested Areas

Chen, Chuanfa; Wang, Mengying; Chang, Bingtao; Li, Yanyan

doi:10.1109/access.2020.2976848

Cited by 16 publications

(8 citation statements)

References 54 publications

(58 reference statements)

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“…The eight datasets (four from the urban and four from the village) were acquired by an OptechALTM scanner (Chen et al, 2020). Fifteen samples were extracted from these eight datasets for later quantitative analysis.…”

Section: Experimental Datamentioning

confidence: 99%

An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data

Chen

Wang²,

He³

et al. 2023

Front. Earth Sci.

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Airborne lidar is a technology for mapping surface spatial information and has been widely used in many areas of geospatial information disciplines. The filtering process of removing non-ground points has always been the focus of research. PTD (Progressive Triangular Irregular Network Densification) filtering algorithm is a widely used filtering algorithm for airborne lidar data. However, this algorithm has shortcomings in retaining ground points in steep areas, leading to large type Ⅰ errors. Therefore, this paper proposes an improved PTD algorithm. The improvement is the addition of the seed points filtering. Specifically, after the potential seed points are obtained by the progressive morphological filter, the seed points filtering is performed on it to remove the non-ground points, so that the obtained seed points are more accurate. The benchmark dataset of ISPRS (International Society for Photogrammetry and Remote Sensing) Working Group III is used to assess the proposed method. Results show that the method is effective in decreasing type Ⅰ error in steep areas. Comparing with the classic PTD algorithm, the type Ⅰ error and total error are decreased by 8.46% and 5.06% respectively. In addition, the proposed method shows a great advantage in computational efficiency, that is eight times more efficient than the classic PTD algorithm.

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Section: Experimental Datamentioning

confidence: 99%

An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data

Chen

Wang²,

He³

et al. 2023

Front. Earth Sci.

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“…Both root mean square error (RMSE) and mean error (ME) [61] were taken as the accuracy measure to assess the accuracy of SRTM3 DEM and the performance of the DEM correction models. RMSE and ME are formulated respectively as ( )…”

Section: Accuracy Measuresmentioning

confidence: 99%

Accuracy Assessment and Correction of SRTM DEM Using ICESat/GLAS Data under Data Coregistration

Chen

Yang

2020

Remote Sensing

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Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) inherently suffers from various errors. Many previous works employed Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) data to assess and enhance SRTM DEM accuracy. Nevertheless, data coregistration between the two datasets was commonly neglected in their studies. In this paper, an automated and simple three dimensional (3D) coregistration method (3CM) was introduced to align the 3-arc-second SRTM (SRTM3) DEM and ICESat/GLAS data over Jiangxi province, China. Then, accuracy evaluation of the SRTM3 DEM using ICESat/GLAS data with and without data coregistration was performed on different classes of terrain factors and different land uses, with the purpose of evaluating the importance of data coregistration. Results show that after data coregistration, the root mean square error (RMSE) and mean bias of the SRTM3 DEM are reduced by 14.4% and 97.1%, respectively. Without data coregistration, terrain aspects with a sine-like shape are strongly related to SRTM3 DEM errors; nevertheless, this relationship disappears after data coregistration. Among the six land uses, SRTM3 DEM produces the lowest accuracy in forest areas. Finally, by incorporating land uses, terrain factors and ICESat/GLAS data into the correction models, the SRTM3 DEM was enhanced using multiple linear regression (MLR), back propagation neural network (BPNN), generalized regression NN (GRNN), and random forest (RF), respectively. Results exhibit that the four enhancement models with data coregistration obviously outperform themselves without the coregistration. Among the four models, RF produces the best result, and its RMSE is about 3.1%, 2.7% and 11.3% lower than those of MLR, BPNN, and GRNN, respectively. Moreover, 146 Global Navigation Satellite System (GNSS) points over Ganzhou city of Jiangxi province were used to assess the accuracy of the RF-derived SRTM3 DEM. It is found that the DEM quality is improved and has a similar error magnitude to that relative to the ICESat/GLASS data.

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“…Consequently, different types of filtering algorithms have been proposed in the past three decades. However, developing a filtering algorithm that is effective and easy to use in various landscapes still remains a challenge [21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…However, CSF also has limitations. First, it is difficult for cloth to cover steep slopes, where accurate reference terrain cannot be obtained to distinguish ground points from non-ground points [26]. For example, cloth particle p still cannot collide with the point q on steep slopes until the end of the simulation (Figure 1a).…”

Section: Introductionmentioning

confidence: 99%

ICSF: An Improved Cloth Simulation Filtering Algorithm for Airborne LiDAR Data Based on Morphological Operations

Cai

Hui

et al. 2023

Forests

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Ground filtering is an essential step in airborne light detection and ranging (LiDAR) data processing in various applications. The cloth simulation filtering (CSF) algorithm has gained popularity because of its ease of use advantage. However, CSF has limitations in topographically and environmentally complex areas. Therefore, an improved CSF (ICSF) algorithm was developed in this study. ICSF uses morphological closing operations to initialize the cloth, and estimates the cloth rigidness for providing a more accurate reference terrain in various terrain characteristics. Moreover, terrain-adaptive height difference thresholds are developed for better filtering of airborne LiDAR point clouds. The performance of ICSF was assessed using International Society for Photogrammetry and Remote Sensing urban and rural samples and Open Topography forested samples. Results showed that ICSF can improve the filtering accuracy of CSF in the samples with various terrain and non-ground object characteristics, while maintaining the ease of use advantage of CSF. In urban and rural samples, ICSF obtained an average total error of 4.03% and outperformed another eight reference algorithms in terms of accuracy and robustness. In forested samples, ICSF produced more accuracy than the well-known filtering algorithms (including the maximum slope, progressive morphology, and cloth simulation filtering algorithms), and performed better with respect to the preservation of steep slopes and discontinuities and vegetation removal. Thus, the proposed algorithm can be used as an efficient tool for LiDAR data processing.

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Multi-Level Interpolation-Based Filter for Airborne LiDAR Point Clouds in Forested Areas

Cited by 16 publications

References 54 publications

An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data

An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data

Accuracy Assessment and Correction of SRTM DEM Using ICESat/GLAS Data under Data Coregistration

ICSF: An Improved Cloth Simulation Filtering Algorithm for Airborne LiDAR Data Based on Morphological Operations

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