An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms

Ding, Kai; Li, Qingquan; Zhu, Jiasong; Wang, Chisheng; Guan, Minglei; Chen, Zhipeng; Yang, Chao; Cui, Yang; Liao, Jianghai

doi:10.3390/s18020552

Cited by 16 publications

(14 citation statements)

References 40 publications

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“…To evaluate the accuracy of the surface and bottom signals, the water LiDAR waveform model (Wa-LiD) presented by Abdallah et al [42] was applied in this experiment. Wa-LiD is a successful simulator for simulating green channel waveforms received from water, which has been widely used in ALB research [15,18,19]. It can perfectly reproduce the received waveforms by adjusting some realistic water parameters [42].…”

Section: Resultsmentioning

confidence: 99%

“…For “DW” waveforms, f C ( t ) is defined as fC2false(tfalse)={exp(fb2+gb+h)(t−ab−a)a<t≤bexp(ft2+gt+h)b<t≤cexp(fc2+gc+h)(d−td−c)c<t≤d0else, where a , b , c and d are the horizontal coordinates of four boundary points in f C1 ( t ), as shown in Figure 10, and f , g and h are coefficients related to water column scattering. Here, an exponential function with a second-order polynomial is proposed to improve the quadrilateral model presented in [19]. Hence, this model is named the exponential function with second-order polynomial model (EFSP).…”

Section: Methodsmentioning

confidence: 99%

“…The triangular function [17] and quadrilateral function [18] were both introduced to water column fitting but were only verified by simulated data. Ding et al [19] proposed an improved quadrilateral model for the water column fitting which shows a better fit to the field data compared with the quadrilateral function. For very shallow water, a surface-volume-bottom (SVB) algorithm was proposed by Schwarz et al [20] and was applied to measure a riverbed.…”

Section: Introductionmentioning

confidence: 99%

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A Depth-Adaptive Waveform Decomposition Method for Airborne LiDAR Bathymetry

Xing

Wang

et al. 2019

Sensors

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Airborne LiDAR bathymetry (ALB) has shown great potential in shallow water and coastal mapping. However, due to the variability of the waveforms, it is hard to detect the signals from the received waveforms with a single algorithm. This study proposed a depth-adaptive waveform decomposition method to fit the waveforms of different depths with different models. In the proposed method, waveforms are divided into two categories based on the water depth, labeled as “shallow water (SW)” and “deep water (DW)”. An empirical waveform model (EW) based on the calibration waveform is constructed for SW waveform decomposition which is more suitable than classical models, and an exponential function with second-order polynomial model (EFSP) is proposed for DW waveform decomposition which performs better than the quadrilateral model. In solving the model’s parameters, a trust region algorithm is introduced to improve the probability of convergence. The proposed method is tested on two field datasets and two simulated datasets to assess the accuracy of the water surface detected in the shallow water and water bottom detected in the deep water. The experimental results show that, compared with the traditional methods, the proposed method performs best, with a high signal detection rate (99.11% in shallow water and 74.64% in deep water), low RMSE (0.09 m for water surface and 0.11 m for water bottom) and wide bathymetric range (0.22 m to 40.49 m).

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Depth-Adaptive Waveform Decomposition Method for Airborne LiDAR Bathymetry

Xing

Wang

et al. 2019

Sensors

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“…Abdallah et al [16] used a triangular function for full waveform processing. Adaby et al [17] and Ding et al [18] presented the combination of a quadrilateral function with Gaussian function. They used three functions: two Gaussian functions for the water surface and the water bottom contribution and a quadrilateral function to fit the water column.…”

mentioning

confidence: 99%

“…The scope of research problems solved thus far indicates cognitive gaps in the use of all data obtained from airborne laser bathymetry for seabed monitoring. Attempts have been made to determine the optimum conditions for obtaining data from ALB [7] and their processing [11,18], yet, a need exists for further research on the analysis of full waveform airborne laser bathymetry and development of the obtained data. Numerous factors such as visibility in water or vegetation have a negative impact on the received signal [25], which results in difficulties in the identification of the second and subsequent returns during full waveform processing.…”

mentioning

confidence: 99%

Improvement of Full Waveform Airborne Laser Bathymetry Data Processing based on Waves of Neighborhood Points

Kogut

Bakuła

2019

Remote Sensing

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Measurements of the topography of the sea floor are one of the main tasks of hydrographic organizations worldwide. The occurrence of any disaster in maritime traffic can contaminate the environment for many years. Therefore, increasing attention is being paid to the development of effective methods for the detection and monitoring of possible obstacles on the transport route. Bathymetric laser scanners record the full waveform reflected from the object (target). Its transformation allows to obtain information about the water surface, water column, seabed, and the objects on it. However, it is not possible to identify subsequent returns among all waves, leading to a loss of information about the situation under the water. On the basis of the studies conducted, it was concluded that the use of a secondary analysis of a full waveform of the airborne laser bathymetry allowed for the identification of objects on the seabed. It allowed us to detect further points in the point cloud, which are necessary in the identification of objects on the seabed. The results of the experiment showed that, among the area of experiment where objects on the seabed were located, the number of points increased between 150 and 550% and the altitude accuracy of the seabed elevation model even by 50% to the level of 0.30 m with reference to sonar data depending of types of objects.

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