Rigorous pulse detection from full-waveform airborne laser scanning data

Lin, Yu‐Ching; Mills, J. P.; Smith-Voysey, Sarah

doi:10.1080/01431160903380599

Cited by 42 publications

(38 citation statements)

References 9 publications

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“…Furthermore, Gaussian decomposition has a vulnerability in that it can result in a null solution or even negative amplitudes in some cases [11,18]. The possibility of failure in the detection of peaks and in the fitting of Gaussian functions to complex waveforms, principally due to a high dependency on local maxima, has been reported [18]. These problems cause the range accuracy to deteriorate, which then impacts upon subsequent lidar processing.…”

Section: Decomposition Methodsmentioning

confidence: 99%

“…In addition, a symmetric function might not always be sufficiently accurate to describe the target characteristics [17,27]. Furthermore, Gaussian decomposition has a vulnerability in that it can result in a null solution or even negative amplitudes in some cases [11,18]. The possibility of failure in the detection of peaks and in the fitting of Gaussian functions to complex waveforms, principally due to a high dependency on local maxima, has been reported [18].…”

Section: Decomposition Methodsmentioning

confidence: 99%

“…However, more accurate results could be achieved when these waveform features were utilized in combination with geometric attributes. Lin et al [18] studied weak and overlapping pulses and reported higher obtainable accuracies in multi-target separation using a rigorous pulse fitting method, however at very high computational cost.…”

Section: Decomposition Methodsmentioning

confidence: 99%

“…However, this method is considered challenging in the case of echoes with low signal strength (low SNR) and it is deficient in its calculation of the cross-section in complex waveforms. The method also requires initial determination of the number of targets [16,18,23,26], which is sometimes impractical or of high computational cost. In addition, a symmetric function might not always be sufficiently accurate to describe the target characteristics [17,27].…”

Section: Decomposition Methodsmentioning

confidence: 99%

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A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

2016

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Full-waveform lidar systems capture the complete backscattered signal from the interaction of the laser beam with targets located within the laser footprint. The resulting data have advantages over discrete return lidar, including higher accuracy of the range measurements and the possibility of retrieving additional returns from weak and overlapping pulses. In addition, radiometric characteristics of targets, e.g., target cross-section, can also be retrieved from the waveforms. However, waveform restoration and removal of the effect of the emitted system pulse from the returned waveform are critical for precise range measurement, 3D reconstruction and target cross-section extraction. In this paper, a sparsity-constrained regularization approach for deconvolution of the returned lidar waveform and restoration of the target cross-section is presented. Primal-dual interior point methods are exploited to solve the resulting nonlinear convex optimization problem. The optimal regularization parameter is determined based on the L-curve method, which provides high consistency in varied conditions. Quantitative evaluation and visual assessment of results show the superior performance of the proposed regularization approach in both removal of the effect of the system waveform and reconstruction of the target cross-section as compared to other prominent deconvolution approaches. This demonstrates the potential of the proposed approach for improving the accuracy of both range measurements and geophysical attribute retrieval. The feasibility and consistency of the presented approach in the processing of a variety of lidar data acquired under different system configurations is also highlighted.

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Section: Decomposition Methodsmentioning

confidence: 99%

Section: Decomposition Methodsmentioning

confidence: 99%

Section: Decomposition Methodsmentioning

confidence: 99%

Section: Decomposition Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

2016

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“…In general, two conventional methods, i.e., the center of gravity and zero-crossing of the first derivative, can be used. Compared with conventional methods, the second derivative algorithm outperforms the two aforementioned traditional methods [40]. The second derivative of the BFWS is calculated as [37] …”

Section: Waveform Decomposition and Differential Gaussian Fittingmentioning

confidence: 99%

A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path

et al. 2017

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A novel de-noising method for improving the performance of full-waveform light detection and ranging (LiDAR) based on differential optical path is proposed, and the mathematical models of this method are developed and verified. Backscattered full-waveform signal (BFWS) is detected by two avalanche photodiodes placed before and after the focus of the focusing lens. On the basis of the proposed method, some simulations are carried out and conclusions are achieved.(1) Background noise can be suppressed effectively and peak points of the BFWS are transformed into negative-going zero-crossing points as stop timing moments. (2) The relative increment percentage of the signal-to-noise ratio based on the proposed method first dramatically increases with the increase of the distance, and then the improvement gets smaller by increasing the distance. (3) The differential Gaussian fitting with the Levenberg-Marquardt algorithm is applied, and the results show that it can decompose the BFWS with high accuracy. (4) The differential distance should not be larger than c/2 × τ rmin , and two variable gain amplifiers can eliminate the inconsistency of two differential beams. The results are beneficial for designing a better performance full-waveform LiDAR.

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Correlation Between Surface Modeling and Pulse Width of FWF-Lidar

Abed

2018

Advances in Remote Sensing and Geo Informatics Applications

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Rigorous pulse detection from full-waveform airborne laser scanning data

Cited by 42 publications

References 9 publications

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path

Correlation Between Surface Modeling and Pulse Width of FWF-Lidar

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