Social stigma and executive compensation

We document that executives working at firms perceived negatively in light of social norms, such as tobacco, gambling and alcohol, earn a significant compensation premium. The premium compensates for personal costs executives bear due to their employer’s negative public perception and include: (i) a reduced likelihood these CEOs will serve as directors on other firms’ boards, which associates with lower executives’ social status, and (ii) impaired job mobility as employers shun stigmatized executives. The compensation premium is not explained by higher managerial skill required in firms we investigate, higher employment contract risk, political capital, litigation risk, or differences in corporate governance quality, and robust to endogeneity concerns. Our results highlight the significant impact job-related social stigma has on executive compensation

Active and Passive Sensor Fusion for Terrestrial Hyperspectral Image Shadow Detection and Restoration

Acquisition of hyperspectral imagery (HSI) from cameras mounted on terrestrial platforms is a relatively recent development that enables spectral analysis of dominantly vertical structures such as geologic outcrops. 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 technique. This dissertation investigates the fusion of terrestrial laser scanning (TLS) spatial information with terrestrial HSI for geometric shadow detection on a vertical outcrop and examines the contribution of radiometrically calibrated TLS intensity, which is resistant to the influence of solar shadowing, to HSI shadow restoration. The proposed method for shadow detection in the terrestrial HSI leverages an accurately georeferenced, high density point cloud acquired with a TLS sensor to geometrically solve for the presence of shadows in the fused HSI. In contrast to traditional methods applied to airborne imagery, the analysis requires a fully 3D mesh representation of the outcrop rather than a 2.5D surface model. The inclusion of radiometrically calibrated TLS intensity in several existing image shadow restoration techniques is examined, and a direct combination of the active TLS and passive HSI radiometric products proposed and evaluated. 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 was found to have a marginal positive influence on restoring shadow spectrum shape, while the proposed combination of TLS intensity with passive HSI spectra boosts restored shadow spectrum magnitude precision by up to 40%, and band correlation with respect to a truth image by up to 45% compared to existing methods. The findings demonstrate that sub-pixel shadow detection in terrestrial HSI can be achieved with geometric methods using standard TLS and HSI field collection practices, and the inclusion of TLS intensity can improve restored HSI spectral characteristics. Simulations incorporating multiple laser wavelengths suggest more robust and computationally efficient methods of combining active and passive spectral data for restoring shadow pixel spectra are possible.Civil and Environmental Engineering, Department o

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

Continuous Coastal Monitoring with an Automated Terrestrial Lidar Scanner

This paper details the collection, geo-referencing, and data processing algorithms for a fully-automated, permanently deployed terrestrial lidar system for coastal monitoring. The lidar is fixed on a 4-m structure located on a shore-backing dune in Duck, North Carolina. Each hour, the lidar collects a three-dimensional framescan of the nearshore region along with a 30-min two-dimensional linescan time series oriented directly offshore, with a linescan repetition rate of approximately 7 Hz. The data are geo-referenced each hour using a rigorous co-registration process that fits 11 fixed planes to a baseline scan to account for small platform movements, and the residual errors from the fit are used to assess the accuracy of the rectification. This process decreased the mean error (defined as the magnitude of the offset in three planes) over a two-year period by 24.41 cm relative to using a fixed rectification matrix. The automated data processing algorithm then filters and grids the data to generate a dry-beach digital elevation model (DEM) from the framescan along with hourly wave runup, hydrodynamic, and morphologic statistics from the linescan time series. The lidar has collected data semi-continuously since January 2015 (with gaps occurring while the lidar was malfunctioning or being serviced), resulting in an hourly data set spanning four years as of January 2019. Examples of data products and potential applications spanning a range of spatial and temporal scales relevant to coastal processes are discussed

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

Radiometric Calibration of an Inexpensive LED-Based Lidar Sensor

Radiometric calibration of laser-based, topographic lidar sensors that measure range via time of flight or phase difference is well established. However, inexpensive, short-range lidar sensors that utilize non-traditional ranging techniques, such as indirect time of flight, may report radiometric quantities that are not appropriate for existing calibration methods. One such lidar sensor is the TeraRanger Evo 60 m by Terabee, whose reported amplitude measurements do not vary smoothly with the amount of return signal power. We investigate the performance of a new radiometric calibration model, one based on a neural network, applied to the Evo 60 m. The proposed model is found to perform similarly to those applied to traditional lidar sensors, with root mean square errors in predicted target reflectance of no more than ±6% for non-specular surfaces. The radiometric calibration model provides a generic approach that may be applicable to other low-cost lidar sensors that report return signal amplitudes that are not smoothly proportional to target range and reflectance

New approach for low-cost TLS target measurement

The registration and calibration of data captured with terrestrial laser scanner instruments can be effectively achieved using signalized targets comprising components of both high and low reflectivity, so-called contrast targets. For projects requiring tens or even hundreds of such targets, the cost of manufacturer-constructed targets can be prohibitive. Moreover, the details of proprietary target center co-ordinate measurement algorithms are often not available to users. This paper reports on the design of a low-cost contrast target using readily-available materials and an accompanying center measurement algorithm. Their compatibility with real terrestrial laser scanner data was extensively tested on six different instruments: two Faro Focus 3D scanners; a Leica HDS6100; a Leica P40; a Riegl VZ-400; and a Zoller+Fröhlich Imager 5010. Repeatability was examined as a function of range, incidence angle, sampling resolution, target intensity and target contrast. Performance in system self-calibration and from independent accuracy assessment is also reported. The results demonstrate compatibility for all five scanners. However, all datasets except the Faro Focus 3D require exclusion of observations made at high incidence angles in order to prevent range biases. Results also demonstrate that the spectral reflectivity of the target components is critical to ensure high contrast between target components and, therefore, high-quality target center co-ordinate measurements.Natural Sciences and Engineering Research Council - Collaborative Research & Development Gran

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

sensor

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