A Comparison of Signal Deconvolution Algorithms Based on Small-Footprint LiDAR Waveform Simulation

Wu, Jiaying; Aardt, Jan A. N. van; Asner, Gregory P.

doi:10.1109/tgrs.2010.2103080

Cited by 79 publications

(53 citation statements)

References 25 publications

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“…For example, Wu et al [21] compared three deconvolution methods: Richardson-Lucy, Wiener filter, and nonnegative least squares to determine the best performance using simulated full waveforms from radiative transfer modeling; the Richardson-Lucy method was found to have superior performance for deconvolution of the simulated full waveforms. Parrish et al [10] presented an empirical technique to compare three different methods for full waveform processing: Gaussian decomposition, Expectation-Maximization (EM) deconvolution and a hybrid method (deconvolve-decompose).…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of High Resolution Airborne Full Waveform LiDAR for Shallow River Bathymetry

et al. 2015

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Abstract:We evaluate the performance of full waveform LiDAR decomposition algorithms with a high-resolution single band airborne LiDAR bathymetry system in shallow rivers. A continuous wavelet transformation (CWT) is proposed and applied in two fluvial environments, and the results are compared to existing echo retrieval methods. LiDAR water depths are also compared to independent field measurements. In both clear and turbid water, the CWT algorithm outperforms the other methods if only green LiDAR observations are available. However, both the definition of the water surface, and the turbidity of the water significantly influence the performance of the LiDAR bathymetry observations. The results suggest that there is no single best full waveform processing algorithm for all bathymetric situations. Overall, the optimal processing strategies resulted in a determination of water depths with a 6 cm mean at 14 cm standard deviation for clear water, and a 16 cm mean and 27 cm standard deviation in more turbid water. OPEN ACCESSRemote Sens. 2015, 7 5134

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Section: Introductionmentioning

confidence: 99%

Performance Assessment of High Resolution Airborne Full Waveform LiDAR for Shallow River Bathymetry

et al. 2015

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“…An encouraging reported approach for both retrieval of the target response and improved range estimation is Wiener filtering [5,12,19,20]. The Wiener filter minimizes the mean square error between the approximated cross-section and the true surface response.…”

Section: Deconvolution Methodsmentioning

confidence: 99%

“…The Wiener filter minimizes the mean square error between the approximated cross-section and the true surface response. However, negative amplitudes and ringing effects usually appear in signals restored by using this method [19,20].…”

Section: Deconvolution Methodsmentioning

confidence: 99%

“…Such approaches can also minimize smearing effects (signal distortion level) on the signal and they have been shown to be effective in retrieving pulses in lidar data [20,26,28], as well as in other fields of research [32]. Wu et al [20] reported that not all peaks can be detected. This can cause errors in range estimation and lead to false target detection.…”

Section: Deconvolution 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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“…Second, additional echo parameters (width, amplitude) can be derived and used in classification tasks (e.g., Wagner et al 2008;Reitberger et al 2008;Heinzel and Koch 2011;Mallet et al 2011). Third, with de-convolution it is possible to derive the target backscatter cross-section that is not blurred by the emitted pulse width and receiver response (Wu et al 2011). The de-convolution is linked with the radiometric calibration presented in Section 1.2.3 (Wagner 2010).…”

Section: Interpretation and Use Of Waveform Lidar Datamentioning

confidence: 99%

Towards an enhanced understanding of airborne LiDAR measurements of forest vegetation

Hovi¹

2015

Diss. For.

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A Comparison of Signal Deconvolution Algorithms Based on Small-Footprint LiDAR Waveform Simulation

Cited by 79 publications

References 25 publications

Performance Assessment of High Resolution Airborne Full Waveform LiDAR for Shallow River Bathymetry

Performance Assessment of High Resolution Airborne Full Waveform LiDAR for Shallow River Bathymetry

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

Towards an enhanced understanding of airborne LiDAR measurements of forest vegetation

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