A Noise Removal Algorithm Based on OPTICS for Photon-Counting LiDAR Data

Zhu, Xinhua; Nie, Sheng; Wang, Cheng; Xi, Xiaohuan; Wang, Jinsong; Dong, Li; Zhou, Hangyu

doi:10.1109/lgrs.2020.3003191

Cited by 73 publications

(34 citation statements)

References 15 publications

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“…Martin et al realized the wide application of density-based spatial clustering application with noise method (DBSCAN) [21], and then a method dominated by density features appeared. The DBSCAN method has been improved by using an ellipsoid [22] unit to divide the point cloud space and by using the accessibility distance [23] as a threshold, but the improved method is complex and high computational demand. The first two density clustering methods have problems in terms of the universality of steep terrain areas and are sensitive to the input parameters of the method.…”

Section: Related Workmentioning

confidence: 99%

“…The relationship between points or between point sets and point sets Statistical outlier removal (SOR) filter [18], [20] Spatial frequency outlier filter [19] Radius outlier removal filtering [12] Density-based spatial clustering application with noise (DBSCAN) [21][22][23] Principal component analysis [14], [24][25][26] This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication.…”

Section: Overall Environment Denoisingmentioning

confidence: 99%

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Noise Point Detection From Airborne LiDAR Point Cloud Based on Spatial Hierarchical Directional Relationship

Lin

Wang

et al. 2022

IEEE Access

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In three-dimensional (3D) airborne light detection and ranging (LiDAR) point-cloud data acquisition, noise point clusters (such as cloud, birds and incomplete scanning ground points) and isolated points are usually generated in the scanning process. Detection and elimination of these noise points directly affect the subsequent processing efficiency of the LiDAR point clouds. In this paper, a noise detection method from airborne LiDAR data based on spatial hierarchical directional relationship and region growing algorithm is proposed. First, the original airborne LiDAR points are divided into regular 3D grids, and the maximum point density unit is searched adaptively to select the initial surface seed points for region growing algorithm. Then, the spherical neighborhood is constructed with the initial seed point as the center, and fourteen main growth directions are generated based on the 3D space topology. Second, candidate seed points in each main direction are determined by the distance threshold. Finally, all LiDAR points are iteratively executed using candidate seed points as new region growing seed points. This paper selects two mountain terrain scenes with different cloud contents as the study area, and the precision, recall rates and F1-score of the proposed method reach 99.8%, 100% and 99.3%, respectively. This method can detect point-cloud clusters and isolated points, thus simplifying the LiDAR point clouds, providing basic support for the subsequent accurate data processing and analysis.

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Section: Related Workmentioning

confidence: 99%

Section: Overall Environment Denoisingmentioning

confidence: 99%

Noise Point Detection From Airborne LiDAR Point Cloud Based on Spatial Hierarchical Directional Relationship

Lin

Wang

et al. 2022

IEEE Access

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“…Improved versions or the combination of these methods were also developed to achieve better results [40,48,53,54,57,58].For instance, The modified histogram based method (MHBM) [57], the official method of ATL03, could be seen the improvement of the HBM [40]. It adopted variable bin size according different surface types, and slope strategy is used in method.…”

Section: ) Adfmmentioning

confidence: 99%

“…This method sets the ratio between the distance along the track and the elevations, and to a certain extent solves the problem of the uneven density of photons in the direction along the track and the elevation, which is an improvement of MDBSCAN [48]. The modified OPTICS method(MOPTICSM) [58] combines the advantages of NFM, MDBSCANN and OPTICS, which continues homogenizing noise photons of NFM, adopts the distance processing method between two points of MDBSCANM [48], and use OPTICS clustering method to complete the denoising. The step of homogenizing noise photons solves the problem of uneven noise distribution, the step of adopting a horizontal ellipse searching area solves the problem of uneven distribution in different directions of photon density, and OPTICS clustering method performs better than DBSCAN method in parameter sensitivity.…”

Section: ) Adfmmentioning

confidence: 99%

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

Xie

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) was launched on September 15, 2018. It is the first photon-counting laser altimeter satellite, which is of great significance for the research into laser altimetry. ICESat-2 is, however, highly sensitive and susceptible to environmental influences. In addition to surface returns, a lot of non-surface 1 photons are found in the data. It is therefore necessary to study an effective method to separate the surface signal from background information. In this paper, we review the existing surface detection methods for photon point cloud data and select seven methods for comparison. Four sources of photon-counting data were considered in the experiments: The Multiple Altimeter Beam Experimental Lidar(MABEL), the Chinese Multi-Beam LiDAR(MBL), The Advanced Topographic Laser Altimeter System(ICESat-2/ATLAS), and MATLAS(using MABEL data to simulate the expected ATLAS photon point cloud). Four scenarios of land, land ice, sea ice, and ocean were also considered. Each surface detection method was tested in 12 experiments, and the different methods were finally compared by qualitative and quantitative measures. We were thus able to establish the advantages and disadvantages of each method, which will be of great significance for scholars studying surface detection methods.

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“…Popescu et al [27] proposed an adaptive ground and canopy height retrieval algorithm based on photon clusters using the simulated ICESat-2 (MABEL) data [25] in different vegetation zones; the performance of the algorithm is less efficient in densely vegetated conditions. Zhu et al [28] presented a modified version of the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm, where the circular shape of the search area in the OPTICS algorithm is modified to an elliptical shape. The algorithm was tested using only the strong beams data in the Saihanba National Nature Reserve (pine forest and grasslands in Hebei, China), using airborne lidar data as a reference.…”

Section: Introductionmentioning

confidence: 99%

Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars

2022

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The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.

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A Noise Removal Algorithm Based on OPTICS for Photon-Counting LiDAR Data

Cited by 73 publications

References 15 publications

Noise Point Detection From Airborne LiDAR Point Cloud Based on Spatial Hierarchical Directional Relationship

Noise Point Detection From Airborne LiDAR Point Cloud Based on Spatial Hierarchical Directional Relationship

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars

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