Zhigang Pan

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.

Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar

In this paper we present a description of a new multispectral airborne mapping light detection and ranging (lidar) along with performance results obtained from two years of data collection and test campaigns. The Titan multiwave lidar is manufactured by Teledyne Optech Inc. (Toronto, ON, Canada) and emits laser pulses in the 1550, 1064 and 532 nm wavelengths simultaneously through a single oscillating mirror scanner at pulse repetition frequencies (PRF) that range from 50 to 300 kHz per wavelength (max combined PRF of 900 kHz). The Titan system can perform simultaneous mapping in terrestrial and very shallow water environments and its multispectral capability enables new applications, such as the production of false color active imagery derived from the lidar return intensities and the automated classification of target and land covers. Field tests and mapping projects performed over the past two years demonstrate capabilities to classify five land covers in urban environments with an accuracy of 90%, map bathymetry under more than 15 m of water, and map thick vegetation canopies at sub-meter vertical resolutions. In addition to its multispectral and performance characteristics, the Titan system is designed with several redundancies and diversity schemes that have proven to be beneficial for both operations and the improvement of data quality.

Estimation of Water Depths and Turbidity From Hyperspectral Imagery Using Support Vector Regression

We propose and evaluate an empirical method for water depth determination from hyperspectral imagery when the benthic layer is visible using support vector regression (SVR). The implementation of the empirical method is presented, and its ability to estimate water depths is compared with a more commonly used band ratio method for two distinct fluvial environments. Our analysis shows that SVR outperforms the band ratio method by providing better root-mean-square error (RMSE) agreement and higher R2 for both clear and turbid water. We also demonstrate an extension of the nonparametric properties of SVR to provide estimates of water turbidity from hyperspectral imagery and show that the approach is able to estimate turbidity with an RMSE of approximately 1.2 NTU when compared with independent turbidity measurements.

A Novel Noise Filtering Model for Photon-Counting Laser Altimeter Data

The new generation of Ice, Cloud, and land Elevation Satellite (ICESat-2) which utilizes photon-counting laser detectors is scheduled for launch in 2017. This upcoming mission will provide data to assess changes of ice sheet elevation and mass, as well as the time-varying volume of sea ice. However, the next-generation ICESat sensor also presents new data processing challenges due to the high number of false returns present in the resultant point cloud that are mainly caused by the high sensitivity of the photon detector to solar returns. In this letter, we propose a novel noise filter for single photon laser altimeter data utilizing a Bayesian decision theory. We applied our algorithm to the Multiple Altimeter Beam Experimental Lidar (MABEL) data sets and compared the filtered estimate of ground to coincident high resolution airborne LiDAR data. The results show that the proposed algorithm differentiates between noise and ground surface returns effectively with 6-m root-mean-square error (RMSE) for the MABEL green channel lasers and 4-m RMSE for the near-infrared channel lasers. The Bayesian approach also outperformed a commonly applied point density-based algorithm, the modified density-based spatial clustering of applications with noise, particularly in areas of steep terrain.

Fusion of LiDAR Orthowaveforms and Hyperspectral Imagery for Shallow River Bathymetry and Turbidity Estimation

We propose an approach to voxelize bathymetric full-waveform LiDAR (Light Detection and Ranging) to generate orthowaveforms and use them to estimate shallow water bathymetry and turbidity with a nonparametric support vector regression (SVR) method. Two distinct shallow rivers were investigated ranging from clear to turbid water; hyperspectral imagery and traditional full-waveform LiDAR processing were also investigated as a baseline for comparison with the proposed orthowaveform strategy. The orthowaveform showed significant correlation to water depth in both scenarios and outperformed hyperspectral imagery for water depth estimation in more turbid water. The orthowaveforms showed similar performance to full-waveform LiDAR point observations for bathymetry estimation in clear water and outperformed the bathymetry performance of full-waveform processing in turbid water. The orthowaveforms also showed similar performance to hyperspectral imagery for predicting water turbidity in turbid water, with a root mean square error (RMSE) of 1.32 NTU. The fusion of both hyperspectral imagery and orthowaveforms was also investigated and gave superior performance to using either data set alone. The fused data set was able to estimate depth in clear and turbid water with an RMSE of 10 and 21 cm, respectively, and turbidity with an RMSE of 1.16 NTU.

An Adaptive Ellipsoid Searching Filter for Airborne Single-Photon Lidar

Recent light detection and ranging (lidar) systems using photon-counting technology are able to collect data with significantly higher efficiency compared with the current commercially available linear-mode lidar systems. However, the high quantum sensitivity of single-photon lidar (SPL) systems results in noisy point clouds due to the influence of solar noise and dark count returns. Therefore, an effective noise removal algorithm is required to interpret SPL data. The uneven distribution of noise returns and the removal of noise close to signal returns are two significant challenges for SPL filtering. In this letter, a novel adaptive ellipsoid searching (AES) method is proposed. The AES uses a spherical noise density estimation model and a morphing ellipsoid determined by local principal components. The proposed method was tested on Sigma Space high-resolution quantum lidar system (HRQLS) SPL data sets and the results were compared with voxel-based filtering of the same data. Independent comparisons of each filtered result with coincident linear-mode airborne lidar data were also undertaken. We find that the root mean square error of the AES results on solid planes is 0.09 versus 0.11 m for voxel-based, 0.12 versus 0.14 m for bare ground, and 2.07 versus 2.55 m for vegetation canopy. We also used manually selected solid planar surfaces as a reference and find that the proposed method successfully removed twice as many noise points as the voxel-based method.

Comparison of bathymetry and seagrass mapping with hyperspectral imagery and airborne bathymetric lidar in a shallow estuarine environment

ABSTRACT We compared hyperspectral imagery and single-wavelength airborne bathymetric light detection and ranging (lidar) for shallow water (<2 m) bathymetry and seagrass mapping. Both the bathymetric results from hyperspectral imagery and airborne bathymetric lidar reveal that the presence of a strongly reflecting benthic layer under seagrass affects the elevation estimates towards the bottom depth instead of the top of seagrass canopy. Full waveform lidar was also investigated for bathymetry and similar performance to discrete lidar was observed. A provisional classification was performed with limited ground reference samples and four supervised classifiers were applied in the study to investigate the capability of airborne bathymetric lidar and hyperspectral imagery to identify seagrass genera. The overall classification accuracy is highly variable and strongly dependent on the classification strategy used. Features from bathymetric lidar alone are not sufficient for substrate classification, while hyperspectral imagery alone showed significant capability for substrate classification with over 95% overall accuracy. The fusion of hyperspectral imagery and bathymetric lidar only marginally improved the overall accuracy of seagrass classification.

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%.

Fusion of bathymetric LiDAR and hyperspectral imagery for shallow water bathymetry

We propose combining a forward model based support vector regression and the semianalytical radiative transfer model to determine shallow water characteristics. The derived water depths were compared to both LiDAR derived water depths and field measured water depths. The bathymetry results show that both LiDAR and hyperspectral imagery are unable to retrieve water depth for deeper water (>7 m) due to the water attenuation. Fusion was also performed with the LiDAR bathymetry as a constraint on the hyperspectral imagery; the constraint varies the estimated water characteristics but we were not able to independently assess the performance because no measurements of water column characteristics were available. The retrieved hyperspectral bathymetry yielded a standard deviation of 20 cm when compared to LiDAR bathymetry.

Adaptive noise filtering for single photon Lidar observations

The large amount of noise returns in single photon Lidar (SPL) point clouds represent a significant challenge for the utilization of these new generation laser scanning systems. Numerous filtering methods have been proposed that attempt to effectively remove noise points from the final point cloud model. However, weak signal points have similar characteristics as noise returns, and thus can be incorrectly eliminated with noise points during the filtering process. Herein, a novel voxel-spherical adaptive ellipsoid searching (VS-AES) method is proposed, by which weak signal returns can be successfully retained while still removing a majority of the noise points. By employing this voxel-spherical (VS) model, our proposed method can simultaneously process a combined SPL data set containing multiple flightlines, in which the noise density is unevenly distributed throughout the whole data set. In addition, an improved adaptive ellipsoid searching (AES) method based on hypothesis testing is developed that is able to remove noise points more robustly than the original version. The experimental results show that the proposed method retains 89.1% of the weak signal point returns from power transmission lines, which is a significant improvement over the performance of either the original AES method (25.9%) or a histogram filtering method (13.4%).