Gaussian Half-Wavelength Progressive Decomposition Method for Waveform Processing of Airborne Laser Bathymetry

Guo, Kai; Xu, Wenxue; Liu, Yanxiong; He, Xiufeng; Tian, Zhen

doi:10.3390/rs10010035

Cited by 27 publications

(13 citation statements)

References 24 publications

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“…The root mean square error (RMSE), correlation coefficient (C), and fit superiority ( 2 ) are chosen as evaluation metrics to quantitatively assess the accuracy of the decomposition results [48]:…”

Section: Comparison Analysis Of Decomposition Accuracymentioning

confidence: 99%

Comparison Analysis of Five Waveform Decomposition Algorithms for the Airborne LiDAR Echo Signal

Zhou

Long

et al. 2021

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

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The information from the components obtained by waveform decomposition is usually used to inverse topography, and classify tree species, etc. Many efforts on waveform decomposition algorithms have been presented, but are lack of comparison analysis and evaluation. Thereby, this paper compares and analyzes the performance of five waveform decomposition algorithms, Gaussian, Adaptive Gaussian, Weibull, Richardson-Lucy (RL), and Gold under different topographic conditions such as forests, glaciers, lakes, and residential areas. The experimental results reveal that (1) the Gaussian algorithm causes the biggest fitting error at 9.96 mV in forested area. It is easy to identify multiple dense peaks as single peaks. (2) There are many misjudged, superimposed, and overlapped waveform components separated by the Weibull algorithm. The Adaptive Gaussian is more capable of fitting complex waveforms, but has 122 more outliers than the Weibull algorithm does. (3) The Gold and RL algorithms decompose the largest number of waveform components (272.2k and 265.9k) in forested area; both RL and Gold algorithms can effectively improve the separability of peaks. (4) The RL algorithm is only more effective for the area with sparse vegetation than the Gold algorithm does, i.e., the Gold algorithm is capable of processing data with dense vegetation areas at a lowest false component detection rate of 1.3%, 0.9%, 1.1%, and 0.1% in four areas. (5) The Gaussian and Gold algorithms have much faster decomposition speed at 1,000/s and 2,000/s than the other three algorithms do. These results are useful for selecting different algorithms under different environments.

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Section: Comparison Analysis Of Decomposition Accuracymentioning

confidence: 99%

Comparison Analysis of Five Waveform Decomposition Algorithms for the Airborne LiDAR Echo Signal

Zhou

Long

et al. 2021

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

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“…The estimated value of the center position c i and the pulse width δ i of each Gaussian component are solved using the positions and intervals of consecutive inflection points. The relationship for the center position c i and pulse width δ i are calculated using Equations ( 6) and (7), respectively [25]. The position and intensity (x max , y max ) of the crest point, the corresponding center position c i and amplitude a i can be determined using Equation (8).…”

Section: Gaussian Component Parameter Solutionmentioning

confidence: 99%

“…For waveform decomposition algorithms, the most commonly used error analysis standard is the root mean square error (RMSE) analysis method, as shown in Equation (12) [25]. We used the RMSE to make a certain comparison between the fitted waveform and the echo waveform, analyze the accuracy of the improved Gaussian decomposition algorithm, and provide a comparison for the subsequent analysis of the accuracy of the hardware structure.…”

Section: Error Analysis Of Echo Waveform Decompositionmentioning

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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“…Goodman et al [15] utilized hyperspectral data to evaluate the performance and sensitivity of a representative semi-analytical inversion model for deriving water depth and benthic surface reflectance. With the development of airborne LiDAR, a series of bathymetry research with higher accuracy has been carried out [16][17][18]. With the development and application of machine learning, especially deep learning models, increasing scholars are beginning to apply machine-learning methods in water depth inversion research.…”

Section: Introductionmentioning

confidence: 99%

Underwater Topography Inversion in Liaodong Shoal Based on GRU Deep Learning Model

Leng

Zhang

et al. 2020

Remote Sensing

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The Liaodong Shoal in the east of the Bohai Sea has obvious water depth variation. The clear shallow water area and deep turbid area coexist, which is characterized by complex submarine topography. The traditional semi-theoretical and semi-empirical models are often difficult to provide optimal inversion results. In this paper, based on the traditional principle of water depth inversion in shallow areas, a new framework is proposed in combination with the deep turbid sea area. This new framework extends the application of traditional optical water depth inversion methods, can meet the needs of the depth inversion work in the composite sea environment. Moreover, the gate recurrent unit (GRU) deep-learning model is introduced to approximate the unified inversion model by numerical calculation. In this paper, based on the above-mentioned inversion framework, the water depth inversion work is processed by using the wide range images of GF-1 satellite, then the relevant analysis and accuracy evaluation are carried out. The results show that: (1) for the overall water depth inversion, the determination coefficient R2 is higher than 0.9 and the MRE is lower than 20% are obtained, and the evaluation index shows that the GRU model can better retrieve the underwater topography of this region. (2) Compared with the traditional log-linear model, Stumpf model, and multi-layer feedforward neural network, the GRU model was significantly improved in various evaluation indices. (3) The model has the best inversion performance in the 24–32 m-depth section, with a MRE of about 4% and a MAE of about 1.42 m, which is more suitable for the inversion work in the comparative section area. (4) The inversion diagram indicates that this model can well reflect the regional seabed characteristics of multiple radial sand ridges, and the overall inversion result is excellent and practical.

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Gaussian Half-Wavelength Progressive Decomposition Method for Waveform Processing of Airborne Laser Bathymetry

Cited by 27 publications

References 24 publications

Comparison Analysis of Five Waveform Decomposition Algorithms for the Airborne LiDAR Echo Signal

Comparison Analysis of Five Waveform Decomposition Algorithms for the Airborne LiDAR Echo Signal

LiDAR Echo Gaussian Decomposition Algorithm for FPGA Implementation

Underwater Topography Inversion in Liaodong Shoal Based on GRU Deep Learning Model

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