Terrain surfaces and 3-D landcover classification from small footprint full-waveform lidar data: application to badlands

Bretar, Frédéric; Chauve, Adrien; Bailly, Jean‐Stéphane; Mallet, Clément; Jacome, Andres

doi:10.5194/hessd-6-151-2009

Cited by 18 publications

(23 citation statements)

References 39 publications

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“…Generally, a set of Gaussian functions is considered to both fit to the received backscattered waveform and to characterize each pulse shape in this approach. Gaussian decomposition and other similar decomposition methods have been extensively used to interpret targets related to the backscattered waveform in urban and forested areas [1,2,17,25]. 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.…”

Section: Decomposition Methodsmentioning

confidence: 99%

“…The general form of the variational regularization, used to retrieve the temporal target cross-section σ in Equation (2), is expressed by Gunturk and Li [35] as:…”

Section: Sparsity-constrained Regularizationmentioning

confidence: 99%

“…In order to recover the target response, the effect of convolution in Equation (2) should be compensated through deconvolution.…”

Section: Target Cross-section Retrievalmentioning

confidence: 99%

“…These include classification [1,2], object extraction and 3D reconstruction [3], vegetation structure characterization [4,5] and digital elevation model (DEM) generation [6].…”

Section: Introductionmentioning

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%

“…The general form of the variational regularization, used to retrieve the temporal target cross-section σ in Equation (2), is expressed by Gunturk and Li [35] as:…”

Section: Sparsity-constrained Regularizationmentioning

confidence: 99%

“…In order to recover the target response, the effect of convolution in Equation (2) should be compensated through deconvolution.…”

Section: Target Cross-section Retrievalmentioning

confidence: 99%

“…These include classification [1,2], object extraction and 3D reconstruction [3], vegetation structure characterization [4,5] and digital elevation model (DEM) generation [6].…”

Section: Introductionmentioning

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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“…SVM is capable of mixing data from diverse sources, responding robustly to dimensionality, and effectively functioning non-linearly in remote sensing applications [37]. The kernel of SVM used in this study was the Gaussian radial basis function.…”

Section: Classificationmentioning

confidence: 99%

Airborne Dual-Wavelength LiDAR Data for Classifying Land Cover

2014

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This study demonstrated the potential of using dual-wavelength airborne light detection and ranging (LiDAR) data to classify land cover. Dual-wavelength LiDAR data were acquired from two airborne LiDAR systems that emitted pulses of light in near-infrared (NIR) and middle-infrared (MIR) lasers. The major features of the LiDAR data, such as surface height, echo width, and dual-wavelength amplitude, were used to represent the characteristics of land cover. Based on the major features of land cover, a support vector machine was used to classify six types of suburban land cover: road and gravel, bare soil, low vegetation, high vegetation, roofs, and water bodies. Results show that using dual-wavelength LiDAR-derived information (e.g., amplitudes at NIR and MIR wavelengths) could compensate for the limitations of using single-wavelength LiDAR information (i.e., poor discrimination of low vegetation) when classifying land cover.

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Mapping of erosion rates in marly badlands based on a coupling of anatomical changes in exposed roots with slope maps derived from LiDAR data

Lopez-Saez¹,

Corona²,

Stoffel

et al. 2011

Earth Surf. Process. Landforms

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Black marls form very extensive outcrops in the Alps and constitute some of the most eroded terrains, thus causing major problems of sedimentation in artificial storage systems (e.g. reservoirs) and river systems. In the experimental catchments near Draix (France), soil erosion rates have been measured in the past at the plot scale through a detailed monitoring of surface elevation changes and at the catchment scale through continuous monitoring of sediment yield in traps at basin outlets. More recently, erosion rates have been determined by means of dendrogeomorphic techniques in three monitored catchments of the Draix basin. A total of 48 exposed roots of Scots pine have been sampled and anatomical variations in annual growth rings resulting from denudation analysed. At the plot scale, average medium-term soil erosion rates derived from exposed roots vary between 1·8 and 13·8 mm yr −1 (average: 5·9 mm yr −1 ) and values are significantly correlated with slope angle. The dendrogeomorphic record of point-scale soil erosion rates matches very well with soil erosion rates measured in the Draix basins. Based on the point-scale measurements and dendrogeomorphic results obtained at the point scale, a linear regression model involving slope angle was derived and coupled to high-resolution slope maps obtained from a LiDAR-generated digital elevation model so as to generate high-resolution soil erosion maps. The resulting regression model is statistically significant and average soil erosion rates obtained from the areal erosion map (5·8, 5·2 and 6·2 mm yr −1 for the Roubine, Moulin and Laval catchments, respectively) prove to be well in concert with average annual erosion rates measured in traps at the outlet of these catchments since 1985 (6·3, 4·1 and 6·4 mm yr −1 ). This contribution demonstrates that dendrogeomorphic analyses of roots clearly have significant potential and that they are a powerful tool for the quantification and mapping of soil erosion rates in areas where measurements of past erosion is lacking.

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Terrain surfaces and 3-D landcover classification from small footprint full-waveform lidar data: application to badlands

Cited by 18 publications

References 39 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

Airborne Dual-Wavelength LiDAR Data for Classifying Land Cover

Mapping of erosion rates in marly badlands based on a coupling of anatomical changes in exposed roots with slope maps derived from LiDAR data

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