Shallow Water Bathymetry Using Full Waveform Bathymetric LiDAR and Hyperspectral Imagery

Date
2016-08
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Abstract

High resolution airborne hyperspectral imagery and high resolution, low pulse energy bathymetric full waveform LiDAR were investigated in this dissertation to investigate their capabilities for predicting shallow water column characteristics and bathymetry.

A continuous waveform transformation method was proposed in this dissertation and compared with other commonly used full waveform processing algorithms. Both single wavelength and dual wavelength bathymetry systems were investigated and the results indicate that a multiwavelength system is superior to a single wavelength for shallow water bathymetry estimation. Significant improvements in point density, multiple return detection, and accuracy were determined for full waveform bathymetric LiDAR.

Support vector regression (SVR) was proposed to retrieve shallow water bathymetry from hyperspectral imagery and compared to an established band ratio method. SVR significantly improved the shallow water bathymetry for the two rivers studied. Water turbidity was also determined from hyperspectral imagery using SVR simultaneously.

The full waveform was further evaluated by using a methodology that voxelizes the original waveforms to generate orthowaveforms that were evaluated for estimating water bathymetry and turbidity. The orthowaveforms outperformed full waveform estimates and were also utilized to retrieve water turbidity. Finally, the fusion of hyperspectral imagery and orthowaveforms was investigated and slightly improved both shallow water bathymetry and water turbidity estimations over using either dataset alone.

The hyperspectral observations were also studied in conjunction with a semi-analytical model to retrieve water column constituent concentrations and bathymetry simultaneously for a coastal region. Both a nonlinear optimization method and a model based SVR method are introduced to estimate water constituents and bathymetry. The bathymetry estimated with these two methods were compared to both bathymetric LiDAR and field measured water depths. The results show both advantages and limitations for hyperspectral imagery bathymetry retrieval. The fusion of bathymetric LiDAR and hyperspectral imagery was also performed; however, the accuracy evaluation was not performed due to the lack of field water constituent concentrations measurements. More studies to optimally fuse these two remote sensing techniques need to be performed in the future.

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Keywords
LiDAR, Hyperspectral imaging, Full waveform, Bathymetry, Water column constituent concentration, Data fusion, Machine learning
Citation
Portions of this document appear in: Legleiter, Carl J., Brandon T. Overstreet, Craig L. Glennie, Zhigang Pan, Juan Carlos Fernandez‐Diaz, and Abhinav Singhania. "Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments." Earth Surface Processes and Landforms 41, no. 3 (2016): 344-363. And in: Pan, Zhigang, Craig Glennie, Carl Legleiter, and Brandon Overstreet. "Estimation of water depths and turbidity from hyperspectral imagery using support vector regression." IEEE Geoscience and Remote Sensing Letters 12, no. 10 (2015): 2165-2169. And in: Pan, Zhigang, Craig Glennie, Preston Hartzell, Juan Carlos Fernandez-Diaz, Carl Legleiter, and Brandon Overstreet. "Performance assessment of high resolution airborne full waveform LiDAR for shallow river bathymetry." Remote Sensing 7, no. 5 (2015): 5133-5159. And in: Pan, Zhigang, Craig L. Glennie, Juan Carlos Fernandez Diaz, Carl J. Legleiter, and Brandon Overstreet. "Fusion of LiDAR Orthowaveforms and Hyperspectral Imagery for Shallow River Bathymetry and Turbidity Estimation." IEEE Trans. Geoscience and Remote Sensing 54, no. 7 (2016): 4165-4177.