Decomposition of laser altimeter waveforms

Hofton, M. A.; Minster, J. B.; Blair, J. B.

doi:10.1109/36.851780

Cited by 301 publications

(195 citation statements)

References 8 publications

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“…The location of the highest two peaks was then used as the mean values ( j ) of the two Gaussian components. Based on the fact that the single Gaussian has two inflection points, the zero crossing of the second derivative was used to obtain the positions of the inflection points of each Gaussian component, and hence the Gaussian's half width (σ j ) was calculated (Hofton et al, 2000).…”

Section: Gaussian Decompositionmentioning

confidence: 99%

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Morsy

Shaker

El-Rabbany

2017

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

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ABSTRACT:With the evolution of the LiDAR technology, multispectral airborne laser scanning systems are currently available. The first operational multispectral airborne LiDAR sensor, the Optech Titan, acquires LiDAR point clouds at three different wavelengths (1.550, 1.064, 0.532 m), allowing the acquisition of different spectral information of land surface. Consequently, the recent studies are devoted to use the radiometric information (i.e., intensity) of the LiDAR data along with the geometric information (e.g., height) for classification purposes. In this study, a data clustering method, based on Gaussian decomposition, is presented. First, a ground filtering mechanism is applied to separate non-ground from ground points. Then, three normalized difference vegetation indices (NDVIs) are computed for both non-ground and ground points, followed by histograms construction from each NDVI. The Gaussian function model is used to decompose the histograms into a number of Gaussian components. The maximum likelihood estimate of the Gaussian components is then optimized using Expectation -Maximization algorithm. The intersection points of the adjacent Gaussian components are subsequently used as threshold values, whereas different classes can be clustered. This method is used to classify the terrain of an urban area in Oshawa, Ontario, Canada, into four main classes, namely roofs, trees, asphalt and grass. It is shown that the proposed method has achieved an overall accuracy up to 95.1% using different NDVIs.

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Section: Gaussian Decompositionmentioning

confidence: 99%

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Morsy

Shaker

El-Rabbany

2017

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

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“…A Gaussian fit to the profile of first return heights was computed as an additional means of characterizing canopy vertical structure (i.e., with the number of Gaussians (NG) used to describe the profile). We followed the approach described in Hofton et al [55] for large-footprint Lidar waveforms. For the Gaussian fit to the ALS canopy height profiles (see example in Figure 2), the single-bin peak at 0 m height (i.e., the forest floor) was not considered so that the Gaussian fit only reflected the vegetation profile.…”

Section: Stand-level Canopy Structure Indicesmentioning

confidence: 99%

Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+

et al. 2012

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Abstract:We have investigated for forest plantations in Chile the stand-level retrieval of canopy height (CH) and growing stock volume (GSV) using Airborne Laser Scanner (ALS), ALOS PALSAR and Landsat. In a two-stage up-scaling approach, ensemble regression tree models (randomForest) were used to relate a suite of ALS canopy structure indices to stand-level in situ measurements of CH and GSV for 319 stands. The retrieval of CH and GSV with ALS yielded high accuracies with R 2 s of 0.93 and 0.81, respectively. A second set of randomForest models was developed using multi-temporal ALOS PALSAR intensities and repeat-pass coherences in two polarizations as well as Landsat data as predictor and stand-level ALS based estimates of CH and GSV as response variables. At three test sites, the retrieval of CH and GSV with PALSAR/Landsat reached promising accuracies with R 2 s in the range of 0.7 to 0.85. We show that the combined use of multi-temporal PALSAR intensity, coherence and Landsat yields higher retrieval accuracies than the retrieval with any of the datasets alone. Potential limitations for the large-area application of the fusion approach included (1) the low sensitivity of ALS first/last return data to forest horizontal structure, affecting the retrieval of GSV in less managed types of forest, and (2) the dense ALS sampling required to achieve high retrieval accuracies at larger scale.

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“…We choose to use a single Gaussian peak to model Y , rather than a mixture. To derive a method that is robust to overlaps (when the left side of the ground peak is contaminated by nearby peaks from low vegetation, animals or objects, received just before the ground return), it seems natural to perform a Gaussian decomposition, using techniques from [14] or [15], then keep the last peak. Unfortunately, not only are these methods complex, as they rely on nonlinear optimization, they are also unstable and lack robustness.…”

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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Decomposition of laser altimeter waveforms

Cited by 301 publications

References 8 publications

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+

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

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