Preston J. Hartzell

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

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.

Empirical Waveform Decomposition and Radiometric Calibration of a Terrestrial Full-Waveform Laser Scanner

The parametric models used in Light Detection And Ranging (LiDAR) waveform decomposition routines are inherently estimates of the sensors system response to backscattered laser pulse power. This estimation can be improved with an empirical system response model, yielding reduced waveform decomposition residuals and more precise echo ranging. We develop an empirical system response model for a Riegl VZ-400 terrestrial laser scanner, from a series of observations to calibrated reflectance targets, and present a numerical least squares method for decomposing waveforms with the model. The target observations are also used to create an empirical radiometric calibration model that accommodates a nonlinear relationship between received optical power and echo peak amplitude, and to examine the temporal stability of the instrument. We find that the least squares waveform decomposition based on the empirical system response model decreases decomposition fitting errors by an order of magnitude for high-amplitude returns and reduces range estimation errors on planar surfaces by 17% over a Gaussian model. The empirical radiometric calibration produces reflectance values self-consistent to within 5% for several materials observed at multiple ranges, and analysis of multiple calibration data sets collected over a one-year period indicates that echo peak amplitude values are stable to within ±3% for target ranges up to 125 m.

Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods

Terrestrial laser scanners are sophisticated instruments that operate much like high-speed total stations. It has previously been shown that unmodelled systematic errors can exist in modern terrestrial laser scanners that deteriorate their geometric measurement precision and accuracy. Typically, signalised targets are used in point-based self-calibrations to identify and model the systematic errors. Although this method has proven its effectiveness, a large quantity of signalised targets is required and is therefore labour-intensive and limits its practicality. In recent years, feature-based self-calibration of aerial, mobile terrestrial, and static terrestrial laser scanning systems has been demonstrated. In this paper, the commonalities and differences between point-based and plane-based self-calibration (in terms of model identification and parameter correlation) are explored. The results of this research indicate that much of the knowledge from point-based self-calibration can be directly transferred to plane-based calibration and that the two calibration approaches are nearly equivalent. New network configurations, such as the inclusion of tilted scans, were also studied and prove to be an effective means for strengthening the self-calibration solution, and improved recoverability of the horizontal collimation axis error for hybrid scanners, which has always posed a challenge in the past.

Comparison of synthetic images generated from LiDAR intensity and passive hyperspectral imagery

Pulsed Light Detection And Ranging (LiDAR) intensity has commonly been used as a measure of relative reflectance of materials to aid in both point classification and object identification. However, as LiDAR systems use a single light wavelength, the intensity has had little value for advanced material classification. With the advent of multispectral LiDAR systems, it may be possible to use the LiDAR intensity in multiple spectral bands to assist in automated target recognition. Towards this end, we present a comparison between LiDAR intensity images and passive reflectance from a hyperspectral imaging system in the same spectral bands. Although qualitatively the LiDAR intensity and hyperspectral imagery show good agreement, a quantitative analysis shows there are significant deviations between their respective reflectance measurements, particularly for complex features such as trees.

Terrestrial Hyperspectral Image Shadow Restoration through Lidar Fusion

Acquisition of hyperspectral imagery (HSI) from cameras mounted on terrestrial platforms is a relatively recent development that enables spectral analysis of dominantly vertical structures. Although solar shadowing is prevalent in terrestrial HSI due to the vertical scene geometry, automated shadow detection and restoration algorithms have not yet been applied to this capture modality. We investigate the fusion of terrestrial laser scanning (TLS) spatial information with terrestrial HSI for geometric shadow detection on a rough vertical surface and examine the contribution of radiometrically calibrated TLS intensity, which is resistant to the influence of solar shadowing, to HSI shadow restoration. Qualitative assessment of the shadow detection results indicates pixel level accuracy, which is indirectly validated by shadow restoration improvements when sub-pixel shadow detection is used in lieu of single pixel detection. The inclusion of TLS intensity in existing shadow restoration algorithms that use regions of matching material in sun and shade exposures was found to have a marginal positive influence on restoring shadow spectrum shape, while a proposed combination of TLS intensity with passive HSI spectra boosts restored shadow spectrum magnitude precision by 40% and band correlation with respect to a truth image by 45% compared to existing restoration methods.

Calibration of an Airborne Single-Photon Lidar System With a Wedge Scanner

Over the past decade, boresight angle calibration of airborne laser scanning (ALS) systems has evolved from ad hoc methods often based on qualitative assessments of point cloud fidelity to rigorous self-calibration algorithms that optimize multiple sensor parameters by minimizing the spatial discrepancies between common features. Although the calibration of linear-mode ALS systems employing oscillating or rotating mirrors has been well developed, little work has addressed the calibration of emergent single-photon lidar (SPL) sensors with circular scan patterns. We adapt a least-squares algorithm employing planar-surface matching to accommodate a spinning wedge prism, employ a synthetic dynamic wedge angle by way of a trigonometric polynomial (TP) to model imperfections in the circular scanning mechanism, and address unique characteristics of SPL data within the stochastic model. Planar fit residuals are reduced by 40% with a boresight and wedge angle adjustment and a further 40% with the introduction of the synthetic wedge angle TP. The addition of the TP also improves the median vertical discrepancy between point clouds generated from fore and aft look angles by over 75%.

Terrestrial hyperspectral image shadow restoration through fusion with terrestrial lidar

Recent advances in remote sensing technology have expanded the acquisition and fusion of active lidar and passive hyperspectral imagery (HSI) from exclusively airborne observations to include terrestrial modalities. In contrast to airborne collection geometry, hyperspectral imagery captured from terrestrial cameras is prone to extensive solar shadowing on vertical surfaces leading to reductions in pixel classification accuracies or outright removal of shadowed areas from subsequent analysis tasks. We demonstrate the use of lidar spatial information for sub-pixel HSI shadow detection and the restoration of shadowed pixel spectra via empirical methods that utilize sunlit and shadowed pixels of similar material composition. We examine the effectiveness of radiometrically calibrated lidar intensity in identifying these similar materials in sun and shade conditions and further evaluate a restoration technique that leverages ratios derived from the overlapping lidar laser and HSI wavelengths. Simulations of multiple lidar wavelengths, i.e., multispectral lidar, indicate the potential for HSI spectral restoration that is independent of the complexity and costs associated with rigorous radiometric transfer models, which have yet to be developed for horizontal-viewing terrestrial HSI sensors. The spectral restoration performance of shadowed HSI pixels is quantified for imagery of a geologic outcrop through improvements in spectral shape, spectral scale, and HSI band correlation.