Decomposition of small-footprint full waveform LiDAR data based on generalized Gaussian model and grouping LM optimization

Ma, Hui; Zhou, Weiwei; Zhang, Liang; Wang, Suyuan

doi:10.1088/1361-6501/aa59f3

Cited by 16 publications

(14 citation statements)

References 23 publications

(19 reference statements)

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“…The proposed method provided an alternative way to improve the decomposition of a full-waveform signal. Different from the newly emergent methods [24][25][26], this method used an iteratively built terrain model to guide the process of full-waveform decomposition and was not sensitive to the initial values.…”

Section: Discussionmentioning

confidence: 99%

“…This method was especially effective with overlapping echo components. In Ma et al, 2017 [25], an advanced optimization method, the Figure 1. An example of a returned full-waveform of a laser pulse passing through a tree (blue points), the modelled ones by fitting the summation of eight and seven Gaussian functions to whole waveform (orange and grey lines, respectively).…”

Section: Introductionmentioning

confidence: 99%

“…This method was especially effective with overlapping echo components. In Ma et al, 2017 [25], an advanced optimization method, the grouping Levenberg-Marquardt (LM) algorithm, was used to fit the generalized Gaussian mixture function to a waveform. The grouping LM optimization method was demonstrated to be less sensitive to the initial parameters compared with the traditional one.…”

Section: Introductionmentioning

confidence: 99%

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An Integrated Approach to Generating Accurate DTM from Airborne Full-Waveform LiDAR Data

Gumerov

Wang

et al. 2017

Remote Sensing

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Abstract:In this study, full-waveform LiDAR data were exploited to detect weak returns backscattered by the bare terrain underneath vegetation canopies and thus improve the generation of a digital terrain model (DTM). Building on the methods of progressive generation of triangulation irregular network (TIN) model reported in the literature, we proposed an integrated approach where echo detection, terrain identification, and TIN generation were carried out iteratively. The proposed method was tested on a dataset collected by a Riegl LMS Q-560 scanner over a study area near Sault Ste. Marie, Ontario, Canada (46 • 33 56 N, 83 • 25 18 W). The results demonstrated that more terrain points under shrubs could be identified, and the generated DTMs exhibited more details in the terrain than those obtained using the progressive TIN method. In addition, 1275 points across this study area were surveyed on the ground and used to validate the proposed approach. The estimated elevations were shown to have a strong linear relationship with the measured ones, with R 2 values above 0.98, and the RMSEs (Root Mean Squared Errors) between them were less than 0.15 m even for areas with hilly terrains underneath vegetation canopies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Integrated Approach to Generating Accurate DTM from Airborne Full-Waveform LiDAR Data

Gumerov

Wang

et al. 2017

Remote Sensing

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“…Airborne laser scanning, also termed airborne Light Detection and Ranging (LiDAR), is an active remote sensing technique for acquiring 3D geospatial data over the Earth’s surface [ 1 , 2 ]. A typical airborne LiDAR system consists of a GPS (Global Positioning System), an IMU (Inertial Measurement Unit), and a laser scanner, with which a point cloud dataset encoding 3D coordinate values under a given geographic coordinate system can be generated [ 3 ]. The point cloud can be further processed to extract thematic information and geo-mapping products, such as manmade objects [ 4 ], stand-alone plants [ 5 ], DEM (Digital Elevation Model)/DTM (Digital Terrain Model) [ 6 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Direct Georeferencing for the Images in an Airborne LiDAR System by Automatic Boresight Misalignments Calibration

Liu

et al. 2020

Sensors

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Airborne Light Detection and Ranging (LiDAR) system and digital camera are usually integrated on a flight platform to obtain multi-source data. However, the photogrammetric system calibration is often independent of the LiDAR system and performed by the aerial triangulation method, which needs a test field with ground control points. In this paper, we present a method for the direct georeferencing of images collected by a digital camera integrated in an airborne LiDAR system by automatic boresight misalignments calibration with the auxiliary of point cloud. The method firstly uses an image matching to generate a tie point set. Space intersection is then performed to obtain the corresponding object coordinate values of the tie points, while the elevation calculated from the space intersection is replaced by the value from the LiDAR data, resulting in a new object point called Virtual Control Point (VCP). Because boresight misalignments exist, a distance between the tie point and the image point of VCP can be found by collinear equations in that image from which the tie point is selected. An iteration process is performed to minimize the distance with boresight corrections in each epoch, and it stops when the distance is smaller than a predefined threshold or the total number of epochs is reached. Two datasets from real projects were used to validate the proposed method and the experimental results show the effectiveness of the method by being evaluated both quantitatively and visually.

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“…As LiDAR data are dependent on several factors, such as the background light and electrical noise from the avalanche photodiode, the selection of an accurate decomposition model and an efficient and fast algorithm is important to ensure accurate and precise results. For a majority of the received signals, amplitude modulation approximates a Gaussian distribution; therefore, waveform signal decomposition based on Gaussian fitting is the most popular method used for LiDAR full-waveform decomposition [9,10]. Hofton et al [11] screened the waveform data according to the importance of the Gaussian component and then solved the optimal value of each waveform component parameter using the nonlinear optimization Levenberg-Marquardt (LM) algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Method for Solving LiDAR Waveform Decomposition Parameters Based on a Variable Projection Algorithm

et al. 2020

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Light detection and ranging (LiDAR) is commonly used to create high-resolution maps; however, the efficiency and convergence of parameter estimation are difficult. To address this issue, we evaluated the structural characteristics of received LiDAR signals by decomposing them into Gaussian functions and applied the variable projection algorithm of the separable nonlinear least-squares problem to the process of waveform fitting. First, using a variable projection algorithm, we separated the linear (amplitude) and nonlinear (center position and width) parameters in the Gaussian function model; the linear parameters are expressed with nonlinear parameters by the function. Thereafter, the optimal estimation of the characteristic parameters of the Gaussian function components was transformed into a least-squares problem only comprising nonlinear parameters. Finally, the Levenberg–Marquardt algorithm was used to solve these nonlinear parameters, whereas the linear parameters were calculated simultaneously in each iteration, and the estimation results satisfying the nonlinear least-square criterion were obtained. Five groups of waveform decomposition simulation data and ICESat/GLAS satellite LiDAR waveform data were used for the parameter estimation experiments. During the experiments, for the same accuracy, the separable nonlinear least-squares optimization method required fewer iterations and lesser calculation time than the traditional method of not separating parameters; the maximum number of iterations was reached before the traditional method converged to the optimal estimate. The method of separating variables only required 14 iterations to obtain the optimal estimate, reducing the computational time from 1128 s to 130 s. Therefore, the application of the separable nonlinear least-squares problem can improve the calculation efficiency and convergence speed of the parameter solution process. It can also provide a new method for parameter estimation in the Gaussian model for LiDAR waveform decomposition.

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Decomposition of small-footprint full waveform LiDAR data based on generalized Gaussian model and grouping LM optimization

Cited by 16 publications

References 23 publications

An Integrated Approach to Generating Accurate DTM from Airborne Full-Waveform LiDAR Data

An Integrated Approach to Generating Accurate DTM from Airborne Full-Waveform LiDAR Data

Direct Georeferencing for the Images in an Airborne LiDAR System by Automatic Boresight Misalignments Calibration

A Method for Solving LiDAR Waveform Decomposition Parameters Based on a Variable Projection Algorithm

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