-spline deconvolution for differential target cross-section determination in full-waveform laser scanning data

Roncat, Andreas; Bergauer, Gunther; Pfeifer, Norbert

doi:10.1016/j.isprsjprs.2011.02.002

Cited by 67 publications

(36 citation statements)

References 15 publications

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“…In [11] the regularization issue is circumvented thanks to redundancy, as the parameter spacing is twice the sampling interval and the bandlimiting effects of the IR is taken into account through the use of B-Splines; the technique is reasonably fast. Due to this spacing, the technique has a behavior similar to the partial deconvolution that we advocate.…”

Section: Partial Deconvolutionmentioning

confidence: 99%

Robust Ground Peak Extraction With Range Error Estimation Using Full-Waveform LiDAR

Jalobeanu

Gonçalves

2014

IEEE Geosci. Remote Sensing Lett.

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Abstract-Topographic mapping is one of the main applications of airborne LiDAR. Waveform digitization and processing allow for both an improved accuracy and a higher ground detection rate compared to discrete return systems. Nevertheless, the quality of the ground peak estimation, based on last return extraction, strongly depends on the algorithm used. Bestperforming methods are too computationally intensive to be used on large datasets. We used Bayesian inference to develop a new ground extraction method whose most original feature is predictive uncertainty computation. It is also fast, and robust to ringing and peak overlaps. Obtaining consistent ranging uncertainties is essential for determining the spatial distribution of error on the final product, point cloud or DEM. The robustness is achieved by a partial deconvolution followed by a Bayesian Gaussian function regression on optimally truncated data, which helps reduce the impact of overlapping peaks from low vegetation. Results from real data are presented, and the gain with respect to classical Gaussian peak fitting is assessed and illustrated.

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Section: Partial Deconvolutionmentioning

confidence: 99%

Robust Ground Peak Extraction With Range Error Estimation Using Full-Waveform LiDAR

Jalobeanu

Gonçalves

2014

IEEE Geosci. Remote Sensing Lett.

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“…It is noted that using the differential cross section, instead of echo width would not necessarily change this. The differential cross section is obtained by deconvolving (Jutzi and Stilla, 2006;Roncat et al, 2011) the received signal with the emitted pulse shape (more precisely the system waveform).…”

Section: Resultsmentioning

confidence: 99%

Potential of Full Waveform Airborne Laser Scanning Data for Urban Area Classification – Transfer of Classification Approaches Between Missions

Tran

Nguyen

Milenković

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

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“…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, parameter estimation based only on the received waveform and not on the perceived deviations in the transmitted pulse may not in general be valid [11]. In addition, direct extraction of the range from the maximal amplitude value is not recommended since the signal pulse may be altered as a result of superimposing different target responses [12,15].…”

Section: Decomposition Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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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-spline deconvolution for differential target cross-section determination in full-waveform laser scanning data

Cited by 67 publications

References 15 publications

Robust Ground Peak Extraction With Range Error Estimation Using Full-Waveform LiDAR

Robust Ground Peak Extraction With Range Error Estimation Using Full-Waveform LiDAR

Potential of Full Waveform Airborne Laser Scanning Data for Urban Area Classification – Transfer of Classification Approaches Between Missions

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

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