A linearly approximated iterative Gaussian decomposition method for waveform LiDAR processing

Mountrakis, Giorgos; Li, Yuguang

doi:10.1016/j.isprsjprs.2017.05.009

Cited by 34 publications

(11 citation statements)

References 60 publications

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“…For establishing the abnormal distance noise, we randomly set the probability of abnormal data, calculate the noise data according to (13) and replace the original data to simulate the influence of distance abnormal noise on the simulation model. The noise level is set to 3, {3, 6,9}…”

Section: Range Profile Model With Noisementioning

confidence: 99%

“…Mountrakis established an alternative decomposition method named Linearly Approximated Iterative Gaussian (LAIGD), whose novelty is that it can follow a multi-step "slow-and-steady" iterative structure, where new Gaussian nodes are quickly discovered and adjusted. Experimental results show that the proposed multi-step method has greatly improved the noise suppression performance and reduces execution times in half [13].…”

Section: Introductionmentioning

confidence: 99%

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Abnormal Noise Suppression and Detail Protection for High-Resolution Range Profile of GM-APD Lidar

Zhao

et al. 2021

IEEE Photonics J.

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The noise suppression of high-resolution range profile (HRRP) is a prerequisite for Geiger-mode of avalanche photodiodes (GM-APD) lidar to achieve precise sensing. However, it is difficult to balance the suppression effect and the integrity of detailed information. Considering this problem, we propose a Bayesian network-based improved loop filter (BNILF) for abnormal noise suppression. Based on the ILF, the Bayesian non-average local filtering model is established to calculate a distance of Pearson distance, which gives the criterion of noise judgment. Furthermore, the block preselection is used to accelerate identify abnormal noise and complete range profile noise suppression. To evaluate the performance of this algorithm, simulation and physical system experiments are carried out. The results show that the proposed algorithm has a better noise suppression effect and a higher detailed information protection ability in comparison with the existing typical approaches.

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Section: Range Profile Model With Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Abnormal Noise Suppression and Detail Protection for High-Resolution Range Profile of GM-APD Lidar

Zhao

et al. 2021

IEEE Photonics J.

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“…Gaussian filtering was carried out for waveform denoising to identify the initial amplitude, width and sigma of the Gaussian decomposition components [51,52]. We then implemented the Gaussian decomposition method to fit the raw waveform from the signal start and end locations using the nonlinear Levenberg-Marquardt (LM) algorithm [52][53][54][55]. By searching forward from the signal endpoint, the center of a Gaussian component with higher amplitude between the last two components was considered the ground position.…”

Section: B Icesat-1 Glas Datamentioning

confidence: 99%

New Metrics and the Combinations for Estimating Forest Biomass From GLAS Data

Zhang

Liang

2021

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

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Geoscience laser altimeter system (GLAS) data have been widely used for forest aboveground biomass (AGB) estimation, but there is no consensus on the optimal metrics. To explore whether a few optimal GLAS metrics could generate accurate AGB estimates, we proposed five metrics and explored their combinations with ten existing ones. The importance of these metrics was measured according to their contributions to changes in the cross-validated mean squared error. The two to eight most important metrics were then selected to develop AGB models, and their performances were evaluated using field AGB. The optimal combination of GLAS metrics were finally used for AGB estimation at GLAS footprints from 2004 to 2007 within a 2°×2°s patial extent in Tahe and Changbai Mountain, China. The results showed that four GLAS metrics, including our proposed metric CRH25 (25th percentile of canopy reflection heights) combined with Lead, QMCH and H75, yield the best AGB estimates, with an R 2 of 0.61±0.15 and RMSE of 52.20±23.50 Mg/ha, and the inclusion of more GLAS metrics did not improve the results. The estimated forest AGB in Tahe was 89.03±19.16 Mg/ha, and 103.07±23.42 Mg/ha in Changbai Mountain. In both regions, the annual average forest AGB estimates for 2005 were higher than the AGB estimates for 2004, 2006, and 2007. The results of this study suggested that a few waveform parameters could enable accurate estimation of forest AGB. Moreover, this study indicated that GLAS data might be able to monitor forest AGB changes, which requires further investigation. Index Terms-forest biomass, GLAS data, waveform parameters I. INTRODUCTION OREST aboveground biomass (AGB) plays an important role in global carbon cycle and climate change studies, but its magnitude, patterns and uncertainties remain poorly quantified [1][2][3]. Over the past decades, the science community has paid much attention to forest AGB estimates from multiple remote sensing datasets, including optical images, synthetic aperture radar (SAR), and light detection and ranging (LiDAR)

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“…Bruggisser et al [15] proposed an algorithm that uses a tilted normal distribution function for waveform decomposition for tree species classification. Mountrakis et al [16] proposed a linear approximate Gaussian decomposition algorithm, also known as the linear approximate iterative Gaussian decomposition (LAIGD) algorithm. Budei et al [17] used Teledyne Optech's multispectral airborne LiDAR (equipped with 1550 nm, 1064 nm, and 532 nm lasers) to identify tree genus or species.…”

Section: Introductionmentioning

confidence: 99%

LiDAR Echo Gaussian Decomposition Algorithm for FPGA Implementation

Zhou

Chen

et al. 2022

Sensors

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As the existing processing algorithms for LiDAR echo decomposition are time-consuming, this paper proposes an FPGA-based improved Gaussian full-waveform decomposition method. The proposed FPGA architecture consists of three modules: (i) a pre-processing module, which is used to pipeline data reading and Gaussian filtering, (ii) the inflection point coordinate solution module, applied to the second-order differential operation and to calculate inflection point coordinates, and (iii) the Gaussian component parameter solution and echo component positioning module, which is utilized to calculate the Gaussian component and echo time parameters. Finally, two LiDAR datasets, covering the Congo and Antarctic regions, are used to verify the accuracy and speed of the proposed method. The experimental results show that (i) the accuracy of the FPGA-based processing is equivalent to that of PC-based processing, and (ii) the processing speed of the FPGA-based processing is 292 times faster than that of PC-based processing.

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A linearly approximated iterative Gaussian decomposition method for waveform LiDAR processing

Cited by 34 publications

References 60 publications

Abnormal Noise Suppression and Detail Protection for High-Resolution Range Profile of GM-APD Lidar

Abnormal Noise Suppression and Detail Protection for High-Resolution Range Profile of GM-APD Lidar

New Metrics and the Combinations for Estimating Forest Biomass From GLAS Data

LiDAR Echo Gaussian Decomposition Algorithm for FPGA Implementation

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