Hyperspectral lidar point cloud segmentation based on geometric and spectral information

Chen, Biwu; Shi, Shuo; Sun, Jiayue; Gong, Wei; Yang, Jian; Du, Lina; Guo, Kuanghui; Wang, Binhui; Chen, Bowen

doi:10.1364/oe.27.024043

Cited by 46 publications

(27 citation statements)

References 59 publications

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“…The feature connectivity control can be further enriched by replacing the verticality with more sophisticated features, or gathering more features. For example, combining radiometric features from new hyper‐spectral LiDAR sensor systems (Chen et al, ) may help to solve the problem reported in Figure .…”

Section: Discussionmentioning

confidence: 99%

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

2020

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Leaf‐wood separation in terrestrial LiDAR data is a prerequisite for non‐destructively estimating biophysical forest properties such as standing wood volumes and leaf area distributions. Current methods have not been extensively applied and tested on tropical trees. Moreover, their impacts on the accuracy of subsequent wood volume retrieval were rarely explored. We present LeWoS, a new fully automatic tool to automate the separation of leaf and wood components, based only on geometric information at both the plot and individual tree scales. This data‐driven method utilizes recursive point cloud segmentation and regularization procedures. Only one parameter is required, which makes our method easily and universally applicable to data from any LiDAR technology and forest type. We conducted a twofold evaluation of the LeWoS method on an extensive dataset of 61 tropical trees. We first assessed the point‐wise classification accuracy, yielding a score of 0.91 ± 0.03 in average. Second, we evaluated the impact of the proposed method on 3D tree models by cross‐comparing estimates in wood volume and branch length with those based on manually separated wood points. This comparison showed similar results, with relative biases of less than 9% and 21% on volume and length respectively. LeWoS allows an automated processing chain for non‐destructive tree volume and biomass estimation when coupled with 3D modelling methods. The average processing time on a laptop was 90s for 1 million points. We provide LeWoS as an open‐source tool with an end‐user interface, together with a large dataset of labelled 3D point clouds from contrasting forest structures. This study closes the gap for stand volume modelling in tropical forests where leaf and wood separation remain a crucial challenge.

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Section: Discussionmentioning

confidence: 99%

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

2020

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“…The distribution density of leaf angle, one of canopy structure parameter, is highly related to the reflectance signals of HSL. Some relevant scholars have carried out preliminary exploration in the correction of factors such as distance and incident angle [45,50,53], but the impact assessment and model correction of complex canopy reflection characteristics need to be further studied.…”

Section: Discussionmentioning

confidence: 99%

“…4). The Spectralon performed high diffuse reflectivity over the ultraviolet, visible, and NIR regions of the spectrum, which are commonly used to obtain target reflectance [50]. With a fixed distance, such as 5.6 m, reference reflectance with this Spectralon was utilized to explore the relationship between HSL spectral intensity and the scanning angle ranging from 0° to 70°.…”

Section: Distance-angle Calibrationmentioning

confidence: 99%

Application of Hyperspectral LiDAR on 3-D Chlorophyll-Nitrogen Mapping of Rohdea Japonica in Laboratory

Du¹,

Jin²,

Chen³

et al. 2021

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

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Biochemicals, such as chlorophyll (Chl) and nitrogen (N), are closely related to photosynthesis process of vegetation. Their accurate estimation is an important topic in remote sensing of vegetation. Previous studies mainly focused on Chl-N content inversion in leaf and canopy level, and few cared about their 3D distributions, which was also an important indicator for the growth status of vegetation (GSV). Hyperspectral LiDAR (HSL) is a novel active remote sensing technology, which has target-sensitive band with hyper-spectra resolution. Its 3D point cloud data simultaneously contains rich spectral and precise geometrical characteristics of the target. This work aims to apply HSL data on 3D Chl-N content mapping in vegetation through constructing HSL-based spectral indices (SIs). Except for following the SI forms of previous works, the normalized differential vegetation index (NDVI) and ratio index (RI) with four-broad-band in an HSL spectral space were successively proposed to invert Chl-N content for the whole vegetation based on the artificial neural network (ANN) method. These four broad-bands were transformed based on the relative spectral response curve of detector and the feature weights (FWs) of multiwavelength respectively. Results show that most HSL-based ANN models can accurately invert Chl-N content with a mean R 2 of >0.75, and some that fusing broad-band data with convolution transformation, namely the FW-based RI, can even obtain a model R 2 of 0.84 for N content inversion. Thus, HSL can be efficiently applied to 3D Chl-N content mapping of vegetation and has great potential in GSV monitoring.

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“…Realizing full-time, full-band, highresolution Earth observation is important in the development of remote sensing technology because it can address many problems in environmental monitoring, urban construction, and military applications. In the process of Earth observation, the simultaneous acquisition and identification of spatial elevation and spectral information is a frontier scientific issue for the development of Earth observation technology in the future [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Parameter Simulation and Design of an Airborne Hyperspectral Imaging LiDAR System

Qian

Song

et al. 2021

Remote Sensing

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With continuous technological development, the future development trend of LiDAR in the field of remote sensing and mapping is to obtain the elevation and spectral information of ground targets simultaneously. Airborne hyperspectral imaging LiDAR inherits the advantages of active and passive remote sensing detection. This paper presents a simulation method to determine the design parameters of an airborne hyperspectral imaging LiDAR system. In accordance with the hyperspectral imaging LiDAR equation and optical design principles, the atmospheric transmission model and the reflectance spectrum of specific ground targets are utilized. The design parameters and laser emission spectrum of the hyperspectral LiDAR system are considered, and the signal-to-noise ratio of the system is obtained through simulation. Without considering the effect of detector gain and electronic amplification on the signal-to-noise ratio, three optical fibers are coupled into a detection channel, and the power spectral density emitted by the supercontinuum laser is simulated by assuming that the signal-to-noise ratio is equal to 1. The power spectral density emitted by the laser must not be less than 15 mW/nm in the shortwave direction. During the simulation process, the design parameters of the hyperspectral LiDAR system are preliminarily demonstrated, and the feasibility of the hyperspectral imaging LiDAR system design is theoretically guaranteed in combination with the design requirements of the supercontinuum laser. The spectral resolution of a single optical fiber of the hyperspectral LiDAR system is set to 2.5 nm. In the actual prototype system, multiple optical fibers can be coupled into a detection channel in accordance with application needs to further improve the signal-to-noise ratio of hyperspectral LiDAR system detection.

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Hyperspectral lidar point cloud segmentation based on geometric and spectral information

Cited by 46 publications

References 59 publications

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

Application of Hyperspectral LiDAR on 3-D Chlorophyll-Nitrogen Mapping of Rohdea Japonica in Laboratory

Parameter Simulation and Design of an Airborne Hyperspectral Imaging LiDAR System

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