© 2016 Dr. Mohsen Azadbakht ; Full-waveform LiDAR systems capture the complete backscattered signal from the interaction of the laser beam with target(s) located within the laser footprint. The resulting data have advantages over discrete return LiDAR, including higher accuracy of the range measurements and the possibility of retrieving additional returns from weak and overlapping pulses. In addition, radiometric characteristics of targets, e.g. target cross-section, can also be retrieved from the waveforms. These capabilities make waveform LiDAR systems advantageous for a broad range of applications. However, waveform restoration and removal of the effect of the emitted system pulse from the returned waveform are critical for precise range measurement, 3D reconstruction and target cross-section extraction. In this thesis, a reliable regularization approach, necessary to deconvolve the returned LiDAR waveform and restore the target cross-section, is presented based on sparsity-constrained regularization. The optimal regularization parameter is determined based on the L-curve and generalized cross validation (GCV) methods, with the former providing higher consistency in varied conditions. A new approach for the estimation of system waveform is presented which is useful when the system waveform is not available in the LiDAR data. The system waveform is approximated based on blind deconvolution over the received LiDAR waveforms of standard flat targets (e.g., asphalt). The reliability of blind deconvolution is compared with existing approaches, with respect to oscillation of the temporal cross-section and the accumulated cross-section value for each pulse. Quantitative evaluation and visual assessment of results are presented in comparison with other prominent deconvolution approaches. The superior performance demonstrates the potential of the proposed regularization approach to remove the effect of system waveform in the returned LiDAR signal and reconstruct the target cross-section, therefore improving the ...