A Multi-Constraint Combined Method for Ground Surface Point Filtering from Mobile LiDAR Point Clouds

Li, Yan; Hua, Liu; Tan, Junxiang; Li, Zan; Chang-jun, Chen

doi:10.3390/rs9090958

Cited by 12 publications

(7 citation statements)

References 26 publications

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“…The point cloud data obtained by TLS are dense and high-precision, and they need to be preprocessed with point cloud decomposition and alignment for subsequent identi fication and separation of the oil tea fruit [51,52]. To improve the accuracy of subsequen point cloud separation, the redundant and noisy data around the oil tea trees were re moved using FARO Scene software, and only the point clouds containing the branches leaves, and fruits of the oil tea trees were retained [53].…”

Section: Identification Of Oil Tea Fruits Point Cloudsmentioning

confidence: 99%

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

et al. 2022

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Oil tea (Camellia oleifera) is one of the world’s major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea forests, and rapid and accurate yield measurement is directly beneficial to efficient oil tea forest management. Light detection and ranging (LiDAR), which can penetrate the canopy to acquire the geometric attributes of targets, has become an effective and popular method of yield identification for agricultural products. However, the common geometric attribute information obtained by LiDAR systems is always limited in terms of the accuracy of yield identification. In this study, to improve yield identification efficiency and accuracy, the red-green-blue (RGB) and luminance-bandwidth-chrominance (i.e., YUV color spaces) were used to identify the point clouds of oil tea fruits. An optimized mean shift clustering algorithm was constructed for oil tea fruit point cloud extraction and product identification. The point cloud data of oil tea trees were obtained using terrestrial laser scanning (TLS), and field measurements were conducted in Changsha County, central China. In addition, the common mean shift, density-based spatial clustering of applications with noise (DBSCAN), and maximum–minimum distance clustering were established for comparison and validation. The results showed that the optimized mean shift clustering algorithm achieved the best identification in both the RGB and YUV color spaces, with detection ratios that were 9.02%, 54.53%, and 3.91% and 7.05%, 62.35%, and 10.78% higher than those of the common mean shift clustering, DBSCAN clustering, and maximum-minimum distance clustering algorithms, respectively. In addition, the improved mean shift clustering algorithm achieved a higher recognition rate in the YUV color space, with an average detection rate of 81.73%, which was 2.4% higher than the average detection rate in the RGB color space. Therefore, this method can perform efficient yield identification of oil tea and provide a new reference for agricultural product management.

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Section: Identification Of Oil Tea Fruits Point Cloudsmentioning

confidence: 99%

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

et al. 2022

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“…Several studies investigated the performance of different ground-filtering algorithms on data acquired by modern MLMS. Such datasets are expected to be very challenging because of the large variation in point density and existence of above-ground objects and land features of various sizes [37]. Serifoglu Yilmaz et al [38] investigated the performance of seven widely used ground-filtering algorithms on UAV-based point clouds from two test sites with different slopes and various sizes of above-ground objects.…”

Section: Drainage Network Extractionmentioning

confidence: 99%

Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

et al. 2021

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Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires a reasonably detailed mapping of the ditch profile to identify areas in need of excavation to remove long-term sediment accumulation. This study utilizes high-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) for mapping roadside ditches and performing hydrological analyses. The performance of alternative MLMS units, including an unmanned aerial vehicle, an unmanned ground vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system, is evaluated. Point clouds from all the MLMS units are in agreement within the ±3 cm range for solid surfaces and ±7 cm range for vegetated areas along the vertical direction. The portable backpack system that could be carried by a surveyor or mounted on a vehicle is found to be the most cost-effective method for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground-filtering approach—cloth simulation—is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from the LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data turned out to be very close to the highway cross slope design standards of 2% on driving lanes, 4% on shoulders, and a 6-by-1 slope for ditch lines.

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“…Compared to ALS, the much higher point density of MLS could provide more detailed 3D spatial information for object classification and environment modeling, such as road surface [50,54,85,150], building [53,76,77], power line [151,152]. However, MLS has the challenges of large data processing.…”

Section: Data Processing Framework Tailored To Mlsmentioning

confidence: 99%

A Survey of Mobile Laser Scanning Applications and Key Techniques over Urban Areas

et al. 2019

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Urban planning and management need accurate three-dimensional (3D) data such as light detection and ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up to millimeter-level accuracy and point density of a few thousand points/m2, have gained increasing attention in urban applications. Substantial research has been conducted in the past decade. This paper conducted a comprehensive survey of urban applications and key techniques based on MLS point clouds. We first introduce the key characteristics of MLS systems and the corresponding point clouds, and present the challenges and opportunities of using the data. Next, we summarize the current applications of using MLS over urban areas, including transportation infrastructure mapping, building information modeling, utility surveying and mapping, vegetation inventory, and autonomous vehicle driving. Then, we review common key issues for processing and analyzing MLS point clouds, including classification methods, object recognition, data registration, data fusion, and 3D city modeling. Finally, we discuss the future prospects for MLS technology and urban applications.

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A Multi-Constraint Combined Method for Ground Surface Point Filtering from Mobile LiDAR Point Clouds

Cited by 12 publications

References 26 publications

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

A Survey of Mobile Laser Scanning Applications and Key Techniques over Urban Areas

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